Accurate parasitic extraction is essential for standard cell analysis, since parasitic capacitance has a direct impact on delay, power, and overall circuit behavior. Conventional extraction approaches often involve a trade-off between accuracy and runtime: rule-based or 2.5D methods are efficient but may lose accuracy on complex layout patterns, while 3D field-solver-based methods provide high fidelity at significantly higher computational cost. Prior work has shown that machine learning can help bridge this gap by enabling fast yet accurate capacitance prediction for standard cell layouts.

This project explores machine learning methods for standard cell parasitic extraction, with a particular focus on capacitance prediction from layout patterns. We are especially interested in deep-learning-based approaches that can improve both accuracy and efficiency, while still benefiting from physical insight. In particular, physics-guided learning and residual-learning strategies have shown strong promise for reducing prediction error and improving robustness in standard cell capacitance extraction.

A central focus of the project is the development and evaluation of deep neural network models, especially DNN- and ResNet-based approaches, for predicting layout-dependent capacitance. Existing studies suggest that deep models are particularly useful in more complex extraction tasks, where simpler methods may fail to capture the relevant trends. The project is therefore well suited for students who are interested in applying modern deep learning methods to practical problems in circuit and layout analysis.

Voraussetzungen

Robust and Explainable Cas13d Guide RNA Design

RNA-targeting CRISPR systems such as Cas13d have emerged as powerful tools for programmable transcriptome engineering. While STOA frameworks improve practical guide RNA design using sequence-only deep learning models and interpretable interfaces, important challenges remain unresolved. Current systems primarily optimize predicted efficacy, while real-world applications require balancing multiple objectives including specificity, robustness across transcripts, structural accessibility, and uncertainty estimation.

This project aims to develop a next-generation design automation framework that performs multi-objective and uncertainty-aware guide RNA optimization for Cas13d systems. The student will investigate how deep learning architectures can jointly model guide efficacy, off-target risk, transcript accessibility, and confidence estimation using only computationally obtainable information.

The project may include:

• designing improved hybrid architectures (Transformer/CNN/attention-based models),
• integrating RNA secondary structure predictions or accessibility priors,
• developing uncertainty-aware ranking methods,
• implementing multi-objective scoring and candidate ranking,
• creating explainability methods for sequence-level interpretation,
• evaluating generalization across transcripts or datasets,
• and optionally extending the interactive natural-language explanation interface.

The final outcome should be a deployable prototype that produces ranked and interpretable guide RNA candidates from raw transcript sequences while improving robustness and transparency in practical CRISPR RNA-targeting workflows.

Possible Research Questions

• Can uncertainty estimation improve trustworthiness in guide RNA ranking?
• How can off-target risk and efficacy be jointly optimized?
• Which sequence motifs or contextual interactions most strongly determine Cas13d activity?
• Can RNA structure-aware representations improve prediction using only computational inputs?
• How transferable are learned guide design principles across datasets or organisms?

Voraussetzungen

The intersection of artificial intelligence and hardware/system design is rapidly evolving. Many traditional problems in areas such as chip design, optimization, and system architecture are being revisited with modern machine learning techniques—yet there is still vast untapped potential for new ideas.

This open topic is aimed at students who want to go beyond predefined projects and instead explore their own research direction. We are particularly interested in novel and creative approaches, including (but not limited to):

• Applying machine learning to classical hardware or EDA problems
• Reinforcement learning or optimization for system design and scheduling
• AI-driven design space exploration or co-design approaches
• Using modern paradigms such as foundation models or autonomous research/optimization agents
• Completely new ideas that challenge existing workflows or assumptions

The goal is to identify promising research directions and develop them into meaningful projects, with the potential to grow into a thesis or even a research publication.

You will work closely with supervision to refine your idea, scope the problem, and develop a concrete research plan—but the starting point should come from you.

Voraussetzungen

Modern chip design faces increasingly complex optimization challenges, where millions of design decisions must be made under tight constraints. One key problem in this space is macro placement: arranging large components (e.g., SRAM blocks, IPs) on a chip such that routing congestion, timing, power delivery, and area are jointly optimized.

This problem is inherently difficult: it involves a highly discrete design space, multiple competing objectives, and strong global dependencies between decisions. Classical methods have been refined for decades, yet recent advances in machine learning—particularly reinforcement learning and graph-based methods—suggest new opportunities for improvement.

In this research internship, you will work on developing and evaluating novel approaches for macro placement and related optimization problems. The focus is on designing efficient algorithms that can handle large-scale, highly constrained systems and produce high-quality solutions under realistic runtime constraints.

Possible directions include:

• learning-based approaches (e.g., reinforcement learning, GNNs),
• hybrid optimization methods combining heuristics and ML,
• scalable search and approximation techniques,
• or improving classical placement strategies with modern tooling.

As part of the project, there is the opportunity to evaluate your approach on a current industry-backed challenge:

https://partcl.com/blog/macro_placement_challenge

(Submissions deadline: May 21, 2026)

While participation in the challenge is optional, it provides a concrete benchmark and external validation for your work. Projects may be conducted in small supervised groups, but individual contributions and reports are required.

The topic is well suited for continuation into a larger thesis project in the area of machine learning for systems and chip design.

Voraussetzungen

Recent advances in AI/ML models (e.g., large language models) demand unprecedented compute and memory resources, making efficient system design a central challenge. New approaches, such as energy-aware co-optimization of hardware architectures and workload execution, enable significant improvements in energy-delay efficiency and scalability .

This thesis builds on such optimization-driven frameworks and opens up a range of possible research directions. Rather than prescribing a fixed path, the goal is to let you explore and define your own contribution within this space, depending on your interests.

Possible directions include (but are not limited to):

• Optimization & Algorithms
  • Improve scalability and efficiency of optimization solvers (e.g., MIQP-based approaches)
  • Develop approximation, heuristic, or hybrid optimization techniques
  • Explore alternative formulations for large-scale design space exploration
• AI for Systems / Learning-Based Methods
  • Investigate reinforcement learning or learning-based approaches for scheduling, mapping, or architecture design
  • Compare learned vs. analytical optimization strategies
• Scalable Systems & Workloads
  • Extend analyses to extremely large workloads (e.g., LLM-scale systems)
  • Study trade-offs between performance, energy, and hardware constraints
• Hardware & Architecture Exploration
  • Analyze emerging architectures such as multi-chiplet systems
  • Explore memory hierarchies, interconnects, and power management strategies
• Tooling & Visualization
  • Develop intuitive interfaces or visual analytics tools for design space exploration
  • Make complex optimization results interpretable and interactive

The work can be adapted toward a more theoretical, systems-oriented, or practical/software-driven thesis. We aim to produce publishable research results, making this an excellent opportunity for students interested in academia or advanced R&D.

Voraussetzungen

Power dissipation of integrated circuits (IC) is crucial, as it directly influences the battery life of edge devices, but also the cooling requirements for servers. To get to power-aware IC designs, precise power modeling is crucial in the design flow. Usually, this is done by a mapping of input features, like input signal activities or the number of gates in the design, to dynamic, static, or total power. Here, recently, machine learning (ML)-based models are in focus. 

The drawback of ML-based models is their limited transferability from circuit designs used in training to unseen circuits. Foundation models, like large language models, have shown great potential in other domains through their generalizability. Hence, they could also support here in the transferability problem of power modeling. Foundation models specifically designed for ICs, like the NetTAG [1], have been proposed. But, it is still open if these complex frameworks provide a significant benefit to power modeling.

The goal of this project is:

- Getting familiar with the NetTAG framework and its adapted version at the chair

- Design a downstream task for power estimation

- Evaluation of the transferability of NetTAG+downstream task

[1] Fang, Wenji, et al. "Nettag: A multimodal rtl-and-layout-aligned netlist foundation model via text-attributed graph." 2025 62nd ACM/IEEE Design Automation Conference (DAC). IEEE, 2025.

Voraussetzungen

Emerging computing architectures increasingly rely on chiplet-based systems and distributed execution to efficiently run complex workloads such as AI inference. In such systems, computations and intermediate results are exchanged between multiple processing units. Ensuring the integrity and correctness of these computations becomes an important challenge, particularly in the presence of hardware faults, communication errors, or malicious manipulation.

Techniques for detecting computational errors have long been studied, for example, through Algorithm-based Fault Tolerance (ABFT) methods for linear algebra operations [1]. More recently, similar concepts have been explored for machine learning workloads and NN inference, where protecting intermediate results and detecting corrupted computations is becoming increasingly important [2], [3], [4], [5]. At the same time, emerging architectures such as chiplet-based systems introduce new challenges for ensuring reliable execution across distributed compute units.

This student project investigates mechanisms for verifying the correctness of distributed AI computations in heterogeneous and chiplet-based architectures. Possible directions include:

• Techniques for integrity verification of distributed AI inference

• Detection of faults or corrupted intermediate results

• Lightweight verification mechanisms based on algorithmic or system-level approaches

• Analysis of trade-offs between reliability, performance, and overhead

References

[1] Kuang-Hua Huang and J. A. Abraham, "Algorithm-Based Fault Tolerance for Matrix Operations," in IEEE Transactions on Computers, vol. C-33, no. 6, pp. 518-528, June 1984, doi: 10.1109/TC.1984.1676475.

[2] S. K. S. Hari, M. B. Sullivan, T. Tsai and S. W. Keckler, "Making Convolutions Resilient Via Algorithm-Based Error Detection Techniques," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 4, pp. 2546-2558, 1 July-Aug. 2022, doi: 10.1109/TDSC.2021.3063083.

[3] J. Hoefer, M. Stammler, F. Kreß, T. Hotfilter, T. Harbaum and J. Becker, "BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring," 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Didcot, United Kingdom, 2024, pp. 1-6, doi: 10.1109/DFT63277.2024.10753546.

[4] Z. Chen, G. Li and K. Pattabiraman, "A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction," in IEEE Design & Test, doi: 10.1109/MDAT.2025.3618758.

[5] J. Kappes, J. Geier, P. van Kempen, D. Mueller-Gritschneder and U. Schlichtmann, "Automated Graph-level Passes for TinyML Fault Tolerance," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-9, doi: 10.1109/IJCNN64981.2025.11227379.

Voraussetzungen

Accurate parasitic extraction is essential for standard cell analysis, since parasitic capacitance has a direct impact on delay, power, and overall circuit behavior. Conventional extraction approaches often involve a trade-off between accuracy and runtime: rule-based or 2.5D methods are efficient but may lose accuracy on complex layout patterns, while 3D field-solver-based methods provide high fidelity at significantly higher computational cost. Prior work has shown that machine learning can help bridge this gap by enabling fast yet accurate capacitance prediction for standard cell layouts.

This project explores machine learning methods for standard cell parasitic extraction, with a particular focus on capacitance prediction from layout patterns. We are especially interested in deep-learning-based approaches that can improve both accuracy and efficiency, while still benefiting from physical insight. In particular, physics-guided learning and residual-learning strategies have shown strong promise for reducing prediction error and improving robustness in standard cell capacitance extraction.

A central focus of the project is the development and evaluation of deep neural network models, especially DNN- and ResNet-based approaches, for predicting layout-dependent capacitance. Existing studies suggest that deep models are particularly useful in more complex extraction tasks, where simpler methods may fail to capture the relevant trends. The project is therefore well suited for students who are interested in applying modern deep learning methods to practical problems in circuit and layout analysis.

Voraussetzungen

Robust and Explainable Cas13d Guide RNA Design

RNA-targeting CRISPR systems such as Cas13d have emerged as powerful tools for programmable transcriptome engineering. While STOA frameworks improve practical guide RNA design using sequence-only deep learning models and interpretable interfaces, important challenges remain unresolved. Current systems primarily optimize predicted efficacy, while real-world applications require balancing multiple objectives including specificity, robustness across transcripts, structural accessibility, and uncertainty estimation.

This project aims to develop a next-generation design automation framework that performs multi-objective and uncertainty-aware guide RNA optimization for Cas13d systems. The student will investigate how deep learning architectures can jointly model guide efficacy, off-target risk, transcript accessibility, and confidence estimation using only computationally obtainable information.

The project may include:

• designing improved hybrid architectures (Transformer/CNN/attention-based models),
• integrating RNA secondary structure predictions or accessibility priors,
• developing uncertainty-aware ranking methods,
• implementing multi-objective scoring and candidate ranking,
• creating explainability methods for sequence-level interpretation,
• evaluating generalization across transcripts or datasets,
• and optionally extending the interactive natural-language explanation interface.

The final outcome should be a deployable prototype that produces ranked and interpretable guide RNA candidates from raw transcript sequences while improving robustness and transparency in practical CRISPR RNA-targeting workflows.

Possible Research Questions

• Can uncertainty estimation improve trustworthiness in guide RNA ranking?
• How can off-target risk and efficacy be jointly optimized?
• Which sequence motifs or contextual interactions most strongly determine Cas13d activity?
• Can RNA structure-aware representations improve prediction using only computational inputs?
• How transferable are learned guide design principles across datasets or organisms?

Voraussetzungen

As data communication demands in many-core systems grow dramatically,networks-on-chip (NoCs) have emerged as an efficient framework for on-chip communication. Electrical Networks-on-Chip (ENoCs) and Wavelength-Routed Optical Networks-on-Chip (WRONoCs) are both considered promising solutions. WRONoCs provide high bandwidth and low latency, while the data transmission of ENoCs is more energy-efficient. To combine their advantages, hybrid Electrical-Optical Networks-on-Chip have been proposed by integrating both transmission paradigms, allowing data to be transmitted through either electrical or optical paths. However, whether such combined architectures can fully realize their potential largely depends on how task mapping is performed. This thesis explores task mapping for the joint optimization of performance, energy, and lifetime in a hybrid optical-electrical NoC architecture.

Voraussetzungen

The intersection of artificial intelligence and hardware/system design is rapidly evolving. Many traditional problems in areas such as chip design, optimization, and system architecture are being revisited with modern machine learning techniques—yet there is still vast untapped potential for new ideas.

This open topic is aimed at students who want to go beyond predefined projects and instead explore their own research direction. We are particularly interested in novel and creative approaches, including (but not limited to):

• Applying machine learning to classical hardware or EDA problems
• Reinforcement learning or optimization for system design and scheduling
• AI-driven design space exploration or co-design approaches
• Using modern paradigms such as foundation models or autonomous research/optimization agents
• Completely new ideas that challenge existing workflows or assumptions

The goal is to identify promising research directions and develop them into meaningful projects, with the potential to grow into a thesis or even a research publication.

You will work closely with supervision to refine your idea, scope the problem, and develop a concrete research plan—but the starting point should come from you.

Voraussetzungen

Modern chip design faces increasingly complex optimization challenges, where millions of design decisions must be made under tight constraints. One key problem in this space is macro placement: arranging large components (e.g., SRAM blocks, IPs) on a chip such that routing congestion, timing, power delivery, and area are jointly optimized.

This problem is inherently difficult: it involves a highly discrete design space, multiple competing objectives, and strong global dependencies between decisions. Classical methods have been refined for decades, yet recent advances in machine learning—particularly reinforcement learning and graph-based methods—suggest new opportunities for improvement.

In this research internship, you will work on developing and evaluating novel approaches for macro placement and related optimization problems. The focus is on designing efficient algorithms that can handle large-scale, highly constrained systems and produce high-quality solutions under realistic runtime constraints.

Possible directions include:

• learning-based approaches (e.g., reinforcement learning, GNNs),
• hybrid optimization methods combining heuristics and ML,
• scalable search and approximation techniques,
• or improving classical placement strategies with modern tooling.

As part of the project, there is the opportunity to evaluate your approach on a current industry-backed challenge:

https://partcl.com/blog/macro_placement_challenge

(Submissions deadline: May 21, 2026)

While participation in the challenge is optional, it provides a concrete benchmark and external validation for your work. Projects may be conducted in small supervised groups, but individual contributions and reports are required.

The topic is well suited for continuation into a larger thesis project in the area of machine learning for systems and chip design.

Voraussetzungen

Recent advances in AI/ML models (e.g., large language models) demand unprecedented compute and memory resources, making efficient system design a central challenge. New approaches, such as energy-aware co-optimization of hardware architectures and workload execution, enable significant improvements in energy-delay efficiency and scalability .

This thesis builds on such optimization-driven frameworks and opens up a range of possible research directions. Rather than prescribing a fixed path, the goal is to let you explore and define your own contribution within this space, depending on your interests.

Possible directions include (but are not limited to):

• Optimization & Algorithms
  • Improve scalability and efficiency of optimization solvers (e.g., MIQP-based approaches)
  • Develop approximation, heuristic, or hybrid optimization techniques
  • Explore alternative formulations for large-scale design space exploration
• AI for Systems / Learning-Based Methods
  • Investigate reinforcement learning or learning-based approaches for scheduling, mapping, or architecture design
  • Compare learned vs. analytical optimization strategies
• Scalable Systems & Workloads
  • Extend analyses to extremely large workloads (e.g., LLM-scale systems)
  • Study trade-offs between performance, energy, and hardware constraints
• Hardware & Architecture Exploration
  • Analyze emerging architectures such as multi-chiplet systems
  • Explore memory hierarchies, interconnects, and power management strategies
• Tooling & Visualization
  • Develop intuitive interfaces or visual analytics tools for design space exploration
  • Make complex optimization results interpretable and interactive

The work can be adapted toward a more theoretical, systems-oriented, or practical/software-driven thesis. We aim to produce publishable research results, making this an excellent opportunity for students interested in academia or advanced R&D.

Voraussetzungen

Emerging computing architectures increasingly rely on chiplet-based systems and distributed execution to efficiently run complex workloads such as AI inference. In such systems, computations and intermediate results are exchanged between multiple processing units. Ensuring the integrity and correctness of these computations becomes an important challenge, particularly in the presence of hardware faults, communication errors, or malicious manipulation.

Techniques for detecting computational errors have long been studied, for example, through Algorithm-based Fault Tolerance (ABFT) methods for linear algebra operations [1]. More recently, similar concepts have been explored for machine learning workloads and NN inference, where protecting intermediate results and detecting corrupted computations is becoming increasingly important [2], [3], [4], [5]. At the same time, emerging architectures such as chiplet-based systems introduce new challenges for ensuring reliable execution across distributed compute units.

This student project investigates mechanisms for verifying the correctness of distributed AI computations in heterogeneous and chiplet-based architectures. Possible directions include:

• Techniques for integrity verification of distributed AI inference

• Detection of faults or corrupted intermediate results

• Lightweight verification mechanisms based on algorithmic or system-level approaches

• Analysis of trade-offs between reliability, performance, and overhead

References

[1] Kuang-Hua Huang and J. A. Abraham, "Algorithm-Based Fault Tolerance for Matrix Operations," in IEEE Transactions on Computers, vol. C-33, no. 6, pp. 518-528, June 1984, doi: 10.1109/TC.1984.1676475.

[2] S. K. S. Hari, M. B. Sullivan, T. Tsai and S. W. Keckler, "Making Convolutions Resilient Via Algorithm-Based Error Detection Techniques," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 4, pp. 2546-2558, 1 July-Aug. 2022, doi: 10.1109/TDSC.2021.3063083.

[3] J. Hoefer, M. Stammler, F. Kreß, T. Hotfilter, T. Harbaum and J. Becker, "BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring," 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Didcot, United Kingdom, 2024, pp. 1-6, doi: 10.1109/DFT63277.2024.10753546.

[4] Z. Chen, G. Li and K. Pattabiraman, "A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction," in IEEE Design & Test, doi: 10.1109/MDAT.2025.3618758.

[5] J. Kappes, J. Geier, P. van Kempen, D. Mueller-Gritschneder and U. Schlichtmann, "Automated Graph-level Passes for TinyML Fault Tolerance," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-9, doi: 10.1109/IJCNN64981.2025.11227379.

Voraussetzungen

Matrix extensions are a key building block for accelerating modern workloads such as machine learning, signal processing, and data analytics. There are tree styles of matrix extensions in development by the RISC-V community:

• Vector-Matrix-Extension (VME)

• Integrated-Matrix-Extension (IME)

• Attached-Matrix-Extension (AME)

In this project, the student will model the proposed IME instructions at the ISA level and integrate them into the ETISS instruction set simulator using the CoreDSL2 ecosystem. This includes defining instruction encodings, assembler syntax, and functional behavior, as well as developing a host-side simulation library to emulate matrix operations.

The extended simulator will be used to evaluate the IME design through testing and benchmarking, allowing analysis of correctness, usability, and performance characteristics. The project provides hands-on experience with ISA design, virtual prototyping, and simulation-based evaluation of emerging hardware extensions, closely aligned with current industrial and academic research.

Tasks

• Study the proposed RISC-V IME specification and analyze differences compared to VME and AME approaches
• ISA-level modeling of IME instructions using CoreDSL2 (instruction encoding, assembler syntax, and semantic behavior)
• Development of a host-side simulation library (based on existing softvector libraries) to emulate matrix operations
• Integration of IME support into the ETISS simulator using the CoreDSL retargeting ecosystem
• Evaluation of the IME extension through testing, benchmarking, and example workloads

Reading Material

• IME Proposal (SpaceMiT): https://github.com/spacemit-com/riscv-ime-extension-spec

• Matrix Extension Proposal (T-Head): https://github.com/XUANTIE-RV/riscv-matrix-extension-spec?tab=readme-ov-file

• IME Mailing List: https://lists.riscv.org/g/tech-integrated-matrix-extension

• IME Charter: https://riscv.atlassian.net/wiki/spaces/IMEX/pages/46071925/Charter
• VME Mailing List:

• VME Charter: https://riscv.atlassian.net/wiki/spaces/VMEX/pages/663912452/Vector-Matrix+Extension+VME+Charter

•  AME Mailing List: https://lists.riscv.org/g/tech-attached-matrix-extension

• AME Charter: https://riscv.atlassian.net/wiki/spaces/AMEX/pages/55083388/Charter

Voraussetzungen

Open-source hardware and EDA tools are increasingly adopted to improve transparency, reproducibility, and technological sovereignty in chip design. RISC-V, as an open instruction set architecture, provides an ideal foundation for building secure and extensible system-on-chip (SoC) platforms. However, implementing complex 64-bit RISC-V SoCs using open-source backend design flows still presents significant challenges in terms of scalability, timing closure, and power efficiency.

This thesis aims to develop and optimize an open-source backend design flow for a secure 64-bit RISC-V SoC. The student will implement a complete backend flow, from synthesized RTL to final layout, using open-source EDA tools. The work will focus on identifying and addressing bottlenecks in the physical design process, such as floorplanning, power distribution, clock tree synthesis, and routing. Security-related design considerations (e.g., integration of security or cryptographic modules) may also be included. The final outcome will be a validated backend flow and a case-study implementation of a secure RISC-V SoC.

Outstanding candidates may be considered for a short-term on-site research stay at TUMCREATE (Singapore), subject to mutual interest and project needs.

Voraussetzungen

Post-quantum cryptography (PQC) is a key enabler for future quantum-safe systems, but the physical realization of PQC hardware modules remains challenging due to their high computational complexity and strict performance and energy constraints. While many PQC algorithms have been studied at the algorithmic and architectural levels, their physical design aspects using open-source EDA tools are still underexplored.

This thesis focuses on the physical implementation and evaluation of PQC hardware modules using an open-source EDA flow. The student will select representative PQC modules (e.g., key computational kernels or full accelerators) and implement them from RTL to layout using open-source tools for synthesis, placement, and routing. Different architectural and implementation configurations will be explored to study their impact on area, timing, and power. The outcome of this work will provide practical insights into the physical design trade-offs of PQC hardware and contribute to open and reproducible chip design methodologies.

Outstanding candidates may be considered for a short-term on-site research stay at TUMCREATE (Singapore), subject to mutual interest and project needs.

Voraussetzungen

As Optical Networks-on-Chip (ONoCs) scale to support an increasing number of cores and diverse communication patterns, a single, uniform router design is often insufficient to achieve optimal performance and energy efficiency. Different communication requirements—such as global data exchange and local traffic—may benefit from different types of optical routers and interconnection structures.

This thesis explores the customized design and composition of optical routers for large-scale ONoC systems. Instead of selecting a single router architecture, the project investigates how different router types (e.g., ring-based routers for global communication and compact routers for local communication) can be combined and deployed to better match application communication patterns. In addition, the thesis will address the task-to-node mapping problem, jointly considering application-level communication behavior and the underlying ONoC structure.

The goal is to develop a co-design framework that integrates router customization, network architecture, and task mapping, enabling more efficient and scalable optical network designs.

Voraussetzungen

Optical Ring Networks-on-Chip (ORNoCs) are widely adopted due to their structural simplicity and scalability. However, the design of ring routers involves tightly coupled decisions between ring waveguide connections and signal path planning, which are often handled separately in existing approaches. This separation can lead to suboptimal designs in terms of performance, power consumption, and physical feasibility.

The objective of this thesis is to develop a design automation framework for ring-based ONoC routers that jointly optimizes ring waveguide connections and signal paths. The problem will be formulated using optimization techniques, with a particular focus on Mixed-Integer Linear Programming (MILP) and algorithmic design. The framework aims to automatically generate efficient and physically feasible router designs under given architectural and physical constraints.

This project is well suited for students interested in optimization, algorithms, and design automation, and provides hands-on experience in translating complex design problems into mathematical models.

Voraussetzungen

One of the crucial tasks in designing, testing, and verifying a digital system is the early estimation of its fault tolerance. For this, fault injection simulations can be used to evaluate this tolerance at different phases of development. At the Register Transfer Level (RTL), an existing but not yet implemented hardware design can be simulated with higher accuracy than its simulation at the instruction or algorithm level. However, accuracy comes at a cost that grows with the number of simulations done within a fault injection analysis. For example, fault injection simulation of a CPU at RTL instead of Instruction Set Architecture (ISA) level would increase the simulation effort to include micro-architectural registers (pipeline, functional units, etc.),

To allow faster fault space exploration at RTL, one could do the following:  (a) accelerate the simulation of an individual Device Under Test (DUT) and fault, or 
  (b) launch multiple fault simulations concurrently

In the case of (a), state-of-the-art research on fast RTL simulations has aimed to reduce simulation cost by multi-threaded simulation on CPUs [1][2][3] or GPUs [4][5][8].

In case (b), multiple independent simulations may be launched simultaneously on a distributed compute cluster. Parallelization of an individual simulation is not as substantial, since the computational platform is utilized anyway, i.e., task-level parallelism of individual simulations for long simulations: one CPU core per fault experiment.

Existing solutions for (b) mainly aim for CPU-based clusters [6][7], whereas for (a), the maximum speed of a single DUT simulation is required. In this work, we want to explore efficient fault simulation at RTL on GPU-based compute clusters that maximizes utilization with the number of individual experiments launched.

Tasks:

1. Set up GPU-accelerated RTL simulation of [8][9]
2. Implement a new fault injection arbitration framework for the RTL simulator
3. Explore multi-experiment partitioning for fault simulation on the new platform
4. Compare and benchmark against a CPU-based fault exploration scheme [6][7]

References:

• [1] W. Snyder, P. Wasson, D. Galbi, et al. Verilator. https://github.com/verilator/verilator, 2019. [Online].
• [2] S. Beamer and D. Donofrio, "Efficiently Exploiting Low Activity Factors to Accelerate RTL Simulation," 2020 57th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2020, pp. 1-6, doi: 10.1109/DAC18072.2020.9218632.
• [3] Kexing Zhou, Yun Liang, Yibo Lin, Runsheng Wang, and Ru Huang. 2023. Khronos: Fusing Memory Access for Improved Hardware RTL Simulation. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '23). Association for Computing Machinery, New York, NY, USA, 180–193. https://doi.org/10.1145/3613424.3614301
• [4] H. Qian and Y. Deng, "Accelerating RTL simulation with GPUs," 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, CA, USA, 2011, pp. 687-693, doi: 10.1109/ICCAD.2011.6105404.
• [5] Dian-Lun Lin, Haoxing Ren, Yanqing Zhang, Brucek Khailany, and Tsung-Wei Huang. 2023. From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus. In Proceedings of the 51st International Conference on Parallel Processing (ICPP '22). Association for Computing Machinery, New York, NY, USA, Article 88, 1–12. https://doi.org/10.1145/3545008.3545091
• [6] Johannes Geier and Daniel Mueller-Gritschneder. 2023. VRTLmod: An LLVM based Open-source Tool to Enable Fault Injection in Verilator RTL Simulations. In Proceedings of the 20th ACM International Conference on Computing Frontiers (CF '23). Association for Computing Machinery, New York, NY, USA, 387–388. https://doi.org/10.1145/3587135.3591435
• [7] J. Geier, L. Kontopoulos, D. Mueller-Gritschneder and U. Schlichtmann, "Rapid Fault Injection Simulation by Hash-Based Differential Fault Effect Equivalence Checks," 2025 Design, Automation & Test in Europe Conference (DATE), Lyon, France, 2025, pp. 1-7, doi: 10.23919/DATE64628.2025.10993266.
• [8] Guo, Zizheng, et al. "GEM: GPU-Accelerated Emulator-Inspired RTL Simulation.https://guozz.cn/publication/gemdac-25/gemdac-25.pdf
• [9] Github NVLabs https://github.com/NVlabs/GEM

Voraussetzungen

Accurate parasitic extraction is essential for standard cell analysis, since parasitic capacitance has a direct impact on delay, power, and overall circuit behavior. Conventional extraction approaches often involve a trade-off between accuracy and runtime: rule-based or 2.5D methods are efficient but may lose accuracy on complex layout patterns, while 3D field-solver-based methods provide high fidelity at significantly higher computational cost. Prior work has shown that machine learning can help bridge this gap by enabling fast yet accurate capacitance prediction for standard cell layouts.

This project explores machine learning methods for standard cell parasitic extraction, with a particular focus on capacitance prediction from layout patterns. We are especially interested in deep-learning-based approaches that can improve both accuracy and efficiency, while still benefiting from physical insight. In particular, physics-guided learning and residual-learning strategies have shown strong promise for reducing prediction error and improving robustness in standard cell capacitance extraction.

A central focus of the project is the development and evaluation of deep neural network models, especially DNN- and ResNet-based approaches, for predicting layout-dependent capacitance. Existing studies suggest that deep models are particularly useful in more complex extraction tasks, where simpler methods may fail to capture the relevant trends. The project is therefore well suited for students who are interested in applying modern deep learning methods to practical problems in circuit and layout analysis.

Voraussetzungen

Robust and Explainable Cas13d Guide RNA Design

RNA-targeting CRISPR systems such as Cas13d have emerged as powerful tools for programmable transcriptome engineering. While STOA frameworks improve practical guide RNA design using sequence-only deep learning models and interpretable interfaces, important challenges remain unresolved. Current systems primarily optimize predicted efficacy, while real-world applications require balancing multiple objectives including specificity, robustness across transcripts, structural accessibility, and uncertainty estimation.

This project aims to develop a next-generation design automation framework that performs multi-objective and uncertainty-aware guide RNA optimization for Cas13d systems. The student will investigate how deep learning architectures can jointly model guide efficacy, off-target risk, transcript accessibility, and confidence estimation using only computationally obtainable information.

The project may include:

• designing improved hybrid architectures (Transformer/CNN/attention-based models),
• integrating RNA secondary structure predictions or accessibility priors,
• developing uncertainty-aware ranking methods,
• implementing multi-objective scoring and candidate ranking,
• creating explainability methods for sequence-level interpretation,
• evaluating generalization across transcripts or datasets,
• and optionally extending the interactive natural-language explanation interface.

The final outcome should be a deployable prototype that produces ranked and interpretable guide RNA candidates from raw transcript sequences while improving robustness and transparency in practical CRISPR RNA-targeting workflows.

Possible Research Questions

• Can uncertainty estimation improve trustworthiness in guide RNA ranking?
• How can off-target risk and efficacy be jointly optimized?
• Which sequence motifs or contextual interactions most strongly determine Cas13d activity?
• Can RNA structure-aware representations improve prediction using only computational inputs?
• How transferable are learned guide design principles across datasets or organisms?

Voraussetzungen

The intersection of artificial intelligence and hardware/system design is rapidly evolving. Many traditional problems in areas such as chip design, optimization, and system architecture are being revisited with modern machine learning techniques—yet there is still vast untapped potential for new ideas.

This open topic is aimed at students who want to go beyond predefined projects and instead explore their own research direction. We are particularly interested in novel and creative approaches, including (but not limited to):

• Applying machine learning to classical hardware or EDA problems
• Reinforcement learning or optimization for system design and scheduling
• AI-driven design space exploration or co-design approaches
• Using modern paradigms such as foundation models or autonomous research/optimization agents
• Completely new ideas that challenge existing workflows or assumptions

The goal is to identify promising research directions and develop them into meaningful projects, with the potential to grow into a thesis or even a research publication.

You will work closely with supervision to refine your idea, scope the problem, and develop a concrete research plan—but the starting point should come from you.

Voraussetzungen

Modern chip design faces increasingly complex optimization challenges, where millions of design decisions must be made under tight constraints. One key problem in this space is macro placement: arranging large components (e.g., SRAM blocks, IPs) on a chip such that routing congestion, timing, power delivery, and area are jointly optimized.

This problem is inherently difficult: it involves a highly discrete design space, multiple competing objectives, and strong global dependencies between decisions. Classical methods have been refined for decades, yet recent advances in machine learning—particularly reinforcement learning and graph-based methods—suggest new opportunities for improvement.

In this research internship, you will work on developing and evaluating novel approaches for macro placement and related optimization problems. The focus is on designing efficient algorithms that can handle large-scale, highly constrained systems and produce high-quality solutions under realistic runtime constraints.

Possible directions include:

• learning-based approaches (e.g., reinforcement learning, GNNs),
• hybrid optimization methods combining heuristics and ML,
• scalable search and approximation techniques,
• or improving classical placement strategies with modern tooling.

As part of the project, there is the opportunity to evaluate your approach on a current industry-backed challenge:

https://partcl.com/blog/macro_placement_challenge

(Submissions deadline: May 21, 2026)

While participation in the challenge is optional, it provides a concrete benchmark and external validation for your work. Projects may be conducted in small supervised groups, but individual contributions and reports are required.

The topic is well suited for continuation into a larger thesis project in the area of machine learning for systems and chip design.

Voraussetzungen

Recent advances in AI/ML models (e.g., large language models) demand unprecedented compute and memory resources, making efficient system design a central challenge. New approaches, such as energy-aware co-optimization of hardware architectures and workload execution, enable significant improvements in energy-delay efficiency and scalability .

This thesis builds on such optimization-driven frameworks and opens up a range of possible research directions. Rather than prescribing a fixed path, the goal is to let you explore and define your own contribution within this space, depending on your interests.

Possible directions include (but are not limited to):

• Optimization & Algorithms
  • Improve scalability and efficiency of optimization solvers (e.g., MIQP-based approaches)
  • Develop approximation, heuristic, or hybrid optimization techniques
  • Explore alternative formulations for large-scale design space exploration
• AI for Systems / Learning-Based Methods
  • Investigate reinforcement learning or learning-based approaches for scheduling, mapping, or architecture design
  • Compare learned vs. analytical optimization strategies
• Scalable Systems & Workloads
  • Extend analyses to extremely large workloads (e.g., LLM-scale systems)
  • Study trade-offs between performance, energy, and hardware constraints
• Hardware & Architecture Exploration
  • Analyze emerging architectures such as multi-chiplet systems
  • Explore memory hierarchies, interconnects, and power management strategies
• Tooling & Visualization
  • Develop intuitive interfaces or visual analytics tools for design space exploration
  • Make complex optimization results interpretable and interactive

The work can be adapted toward a more theoretical, systems-oriented, or practical/software-driven thesis. We aim to produce publishable research results, making this an excellent opportunity for students interested in academia or advanced R&D.

Voraussetzungen

One of the crucial tasks in designing, testing, and verifying a digital system is the early estimation of its fault tolerance. For this, fault injection simulations can be used to evaluate this tolerance at different phases of development. At the Register Transfer Level (RTL), an existing but not yet implemented hardware design can be simulated with higher accuracy than its simulation at the instruction or algorithm level. However, accuracy comes at a cost that grows with the number of simulations done within a fault injection analysis. For example, fault injection simulation of a CPU at RTL instead of Instruction Set Architecture (ISA) level would increase the simulation effort to include micro-architectural registers (pipeline, functional units, etc.),

To allow faster fault space exploration at RTL, one could do the following:  (a) accelerate the simulation of an individual Device Under Test (DUT) and fault, or 
  (b) launch multiple fault simulations concurrently

In the case of (a), state-of-the-art research on fast RTL simulations has aimed to reduce simulation cost by multi-threaded simulation on CPUs [1][2][3] or GPUs [4][5][8].

In case (b), multiple independent simulations may be launched simultaneously on a distributed compute cluster. Parallelization of an individual simulation is not as substantial, since the computational platform is utilized anyway, i.e., task-level parallelism of individual simulations for long simulations: one CPU core per fault experiment.

Existing solutions for (b) mainly aim for CPU-based clusters [6][7], whereas for (a), the maximum speed of a single DUT simulation is required. In this work, we want to explore efficient fault simulation at RTL on GPU-based compute clusters that maximizes utilization with the number of individual experiments launched.

Tasks:

1. Set up GPU-accelerated RTL simulation of [8][9]
2. Implement a new fault injection arbitration framework for the RTL simulator
3. Explore multi-experiment partitioning for fault simulation on the new platform
4. Compare and benchmark against a CPU-based fault exploration scheme [6][7]

References:

• [1] W. Snyder, P. Wasson, D. Galbi, et al. Verilator. https://github.com/verilator/verilator, 2019. [Online].
• [2] S. Beamer and D. Donofrio, "Efficiently Exploiting Low Activity Factors to Accelerate RTL Simulation," 2020 57th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2020, pp. 1-6, doi: 10.1109/DAC18072.2020.9218632.
• [3] Kexing Zhou, Yun Liang, Yibo Lin, Runsheng Wang, and Ru Huang. 2023. Khronos: Fusing Memory Access for Improved Hardware RTL Simulation. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '23). Association for Computing Machinery, New York, NY, USA, 180–193. https://doi.org/10.1145/3613424.3614301
• [4] H. Qian and Y. Deng, "Accelerating RTL simulation with GPUs," 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, CA, USA, 2011, pp. 687-693, doi: 10.1109/ICCAD.2011.6105404.
• [5] Dian-Lun Lin, Haoxing Ren, Yanqing Zhang, Brucek Khailany, and Tsung-Wei Huang. 2023. From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus. In Proceedings of the 51st International Conference on Parallel Processing (ICPP '22). Association for Computing Machinery, New York, NY, USA, Article 88, 1–12. https://doi.org/10.1145/3545008.3545091
• [6] Johannes Geier and Daniel Mueller-Gritschneder. 2023. VRTLmod: An LLVM based Open-source Tool to Enable Fault Injection in Verilator RTL Simulations. In Proceedings of the 20th ACM International Conference on Computing Frontiers (CF '23). Association for Computing Machinery, New York, NY, USA, 387–388. https://doi.org/10.1145/3587135.3591435
• [7] J. Geier, L. Kontopoulos, D. Mueller-Gritschneder and U. Schlichtmann, "Rapid Fault Injection Simulation by Hash-Based Differential Fault Effect Equivalence Checks," 2025 Design, Automation & Test in Europe Conference (DATE), Lyon, France, 2025, pp. 1-7, doi: 10.23919/DATE64628.2025.10993266.
• [8] Guo, Zizheng, et al. "GEM: GPU-Accelerated Emulator-Inspired RTL Simulation.https://guozz.cn/publication/gemdac-25/gemdac-25.pdf
• [9] Github NVLabs https://github.com/NVlabs/GEM

Voraussetzungen

Accurate parasitic extraction is essential for standard cell analysis, since parasitic capacitance has a direct impact on delay, power, and overall circuit behavior. Conventional extraction approaches often involve a trade-off between accuracy and runtime: rule-based or 2.5D methods are efficient but may lose accuracy on complex layout patterns, while 3D field-solver-based methods provide high fidelity at significantly higher computational cost. Prior work has shown that machine learning can help bridge this gap by enabling fast yet accurate capacitance prediction for standard cell layouts.

This project explores machine learning methods for standard cell parasitic extraction, with a particular focus on capacitance prediction from layout patterns. We are especially interested in deep-learning-based approaches that can improve both accuracy and efficiency, while still benefiting from physical insight. In particular, physics-guided learning and residual-learning strategies have shown strong promise for reducing prediction error and improving robustness in standard cell capacitance extraction.

A central focus of the project is the development and evaluation of deep neural network models, especially DNN- and ResNet-based approaches, for predicting layout-dependent capacitance. Existing studies suggest that deep models are particularly useful in more complex extraction tasks, where simpler methods may fail to capture the relevant trends. The project is therefore well suited for students who are interested in applying modern deep learning methods to practical problems in circuit and layout analysis.

Voraussetzungen

Robust and Explainable Cas13d Guide RNA Design

RNA-targeting CRISPR systems such as Cas13d have emerged as powerful tools for programmable transcriptome engineering. While STOA frameworks improve practical guide RNA design using sequence-only deep learning models and interpretable interfaces, important challenges remain unresolved. Current systems primarily optimize predicted efficacy, while real-world applications require balancing multiple objectives including specificity, robustness across transcripts, structural accessibility, and uncertainty estimation.

This project aims to develop a next-generation design automation framework that performs multi-objective and uncertainty-aware guide RNA optimization for Cas13d systems. The student will investigate how deep learning architectures can jointly model guide efficacy, off-target risk, transcript accessibility, and confidence estimation using only computationally obtainable information.

The project may include:

• designing improved hybrid architectures (Transformer/CNN/attention-based models),
• integrating RNA secondary structure predictions or accessibility priors,
• developing uncertainty-aware ranking methods,
• implementing multi-objective scoring and candidate ranking,
• creating explainability methods for sequence-level interpretation,
• evaluating generalization across transcripts or datasets,
• and optionally extending the interactive natural-language explanation interface.

The final outcome should be a deployable prototype that produces ranked and interpretable guide RNA candidates from raw transcript sequences while improving robustness and transparency in practical CRISPR RNA-targeting workflows.

Possible Research Questions

• Can uncertainty estimation improve trustworthiness in guide RNA ranking?
• How can off-target risk and efficacy be jointly optimized?
• Which sequence motifs or contextual interactions most strongly determine Cas13d activity?
• Can RNA structure-aware representations improve prediction using only computational inputs?
• How transferable are learned guide design principles across datasets or organisms?

Voraussetzungen

The intersection of artificial intelligence and hardware/system design is rapidly evolving. Many traditional problems in areas such as chip design, optimization, and system architecture are being revisited with modern machine learning techniques—yet there is still vast untapped potential for new ideas.

This open topic is aimed at students who want to go beyond predefined projects and instead explore their own research direction. We are particularly interested in novel and creative approaches, including (but not limited to):

• Applying machine learning to classical hardware or EDA problems
• Reinforcement learning or optimization for system design and scheduling
• AI-driven design space exploration or co-design approaches
• Using modern paradigms such as foundation models or autonomous research/optimization agents
• Completely new ideas that challenge existing workflows or assumptions

The goal is to identify promising research directions and develop them into meaningful projects, with the potential to grow into a thesis or even a research publication.

You will work closely with supervision to refine your idea, scope the problem, and develop a concrete research plan—but the starting point should come from you.

Voraussetzungen

Modern chip design faces increasingly complex optimization challenges, where millions of design decisions must be made under tight constraints. One key problem in this space is macro placement: arranging large components (e.g., SRAM blocks, IPs) on a chip such that routing congestion, timing, power delivery, and area are jointly optimized.

This problem is inherently difficult: it involves a highly discrete design space, multiple competing objectives, and strong global dependencies between decisions. Classical methods have been refined for decades, yet recent advances in machine learning—particularly reinforcement learning and graph-based methods—suggest new opportunities for improvement.

In this research internship, you will work on developing and evaluating novel approaches for macro placement and related optimization problems. The focus is on designing efficient algorithms that can handle large-scale, highly constrained systems and produce high-quality solutions under realistic runtime constraints.

Possible directions include:

• learning-based approaches (e.g., reinforcement learning, GNNs),
• hybrid optimization methods combining heuristics and ML,
• scalable search and approximation techniques,
• or improving classical placement strategies with modern tooling.

As part of the project, there is the opportunity to evaluate your approach on a current industry-backed challenge:

https://partcl.com/blog/macro_placement_challenge

(Submissions deadline: May 21, 2026)

While participation in the challenge is optional, it provides a concrete benchmark and external validation for your work. Projects may be conducted in small supervised groups, but individual contributions and reports are required.

The topic is well suited for continuation into a larger thesis project in the area of machine learning for systems and chip design.

Voraussetzungen

Large language models continue to push modern hardware systems to their limits. In particular, data movement, memory access costs, and energy efficiency have become central bottlenecks for scalable inference. Compute-in-memory architectures offer a promising direction to address these challenges by bringing computation closer to where data resides and thereby reducing the overhead of conventional processor-centric systems.

In this research internship, you will contribute to an ongoing project on compute-in-memory architectures for LLM inference. The focus will be on deepening and sharpening the current research by analyzing recent developments, strengthening technical comparisons, refining system-level insights, and helping consolidate the latest progress in this rapidly evolving area.

This may include:

• studying recent work on efficient LLM inference and compute-in-memory systems,
• comparing architectural approaches and identifying key design trade-offs,
• helping structure and sharpen technical arguments and insights,
• supporting the analysis of hardware, performance, and energy-efficiency aspects,
• and contributing to the further development of a research manuscript.

The internship is particularly well suited for students who are interested in academia and would like to gain hands-on experience in how research is developed and turned into a strong scientific outcome. You will work on a highly relevant topic with clear practical and research impact, and gain insight into current questions in next-generation AI hardware.

Voraussetzungen

Recent advances in AI/ML models (e.g., large language models) demand unprecedented compute and memory resources, making efficient system design a central challenge. New approaches, such as energy-aware co-optimization of hardware architectures and workload execution, enable significant improvements in energy-delay efficiency and scalability .

This thesis builds on such optimization-driven frameworks and opens up a range of possible research directions. Rather than prescribing a fixed path, the goal is to let you explore and define your own contribution within this space, depending on your interests.

Possible directions include (but are not limited to):

• Optimization & Algorithms
  • Improve scalability and efficiency of optimization solvers (e.g., MIQP-based approaches)
  • Develop approximation, heuristic, or hybrid optimization techniques
  • Explore alternative formulations for large-scale design space exploration
• AI for Systems / Learning-Based Methods
  • Investigate reinforcement learning or learning-based approaches for scheduling, mapping, or architecture design
  • Compare learned vs. analytical optimization strategies
• Scalable Systems & Workloads
  • Extend analyses to extremely large workloads (e.g., LLM-scale systems)
  • Study trade-offs between performance, energy, and hardware constraints
• Hardware & Architecture Exploration
  • Analyze emerging architectures such as multi-chiplet systems
  • Explore memory hierarchies, interconnects, and power management strategies
• Tooling & Visualization
  • Develop intuitive interfaces or visual analytics tools for design space exploration
  • Make complex optimization results interpretable and interactive

The work can be adapted toward a more theoretical, systems-oriented, or practical/software-driven thesis. We aim to produce publishable research results, making this an excellent opportunity for students interested in academia or advanced R&D.

Voraussetzungen

Power dissipation of integrated circuits (IC) is crucial, as it directly influences the battery life of edge devices, but also the cooling requirements for servers. To get to power-aware IC designs, precise power modeling is crucial in the design flow. Usually, this is done by a mapping of input features, like input signal activities or the number of gates in the design, to dynamic, static, or total power. Here, recently, machine learning (ML)-based models are in focus. 

The drawback of ML-based models is their limited transferability from circuit designs used in training to unseen circuits. Foundation models, like large language models, have shown great potential in other domains through their generalizability. Hence, they could also support here in the transferability problem of power modeling. Foundation models specifically designed for ICs, like the NetTAG [1], have been proposed. But, it is still open if these complex frameworks provide a significant benefit to power modeling.

The goal of this project is:

- Getting familiar with the NetTAG framework and its adapted version at the chair

- Design a downstream task for power estimation

- Evaluation of the transferability of NetTAG+downstream task

[1] Fang, Wenji, et al. "Nettag: A multimodal rtl-and-layout-aligned netlist foundation model via text-attributed graph." 2025 62nd ACM/IEEE Design Automation Conference (DAC). IEEE, 2025.

Voraussetzungen

Emerging computing architectures increasingly rely on chiplet-based systems and distributed execution to efficiently run complex workloads such as AI inference. In such systems, computations and intermediate results are exchanged between multiple processing units. Ensuring the integrity and correctness of these computations becomes an important challenge, particularly in the presence of hardware faults, communication errors, or malicious manipulation.

Techniques for detecting computational errors have long been studied, for example, through Algorithm-based Fault Tolerance (ABFT) methods for linear algebra operations [1]. More recently, similar concepts have been explored for machine learning workloads and NN inference, where protecting intermediate results and detecting corrupted computations is becoming increasingly important [2], [3], [4], [5]. At the same time, emerging architectures such as chiplet-based systems introduce new challenges for ensuring reliable execution across distributed compute units.

This student project investigates mechanisms for verifying the correctness of distributed AI computations in heterogeneous and chiplet-based architectures. Possible directions include:

• Techniques for integrity verification of distributed AI inference

• Detection of faults or corrupted intermediate results

• Lightweight verification mechanisms based on algorithmic or system-level approaches

• Analysis of trade-offs between reliability, performance, and overhead

References

[1] Kuang-Hua Huang and J. A. Abraham, "Algorithm-Based Fault Tolerance for Matrix Operations," in IEEE Transactions on Computers, vol. C-33, no. 6, pp. 518-528, June 1984, doi: 10.1109/TC.1984.1676475.

[2] S. K. S. Hari, M. B. Sullivan, T. Tsai and S. W. Keckler, "Making Convolutions Resilient Via Algorithm-Based Error Detection Techniques," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 4, pp. 2546-2558, 1 July-Aug. 2022, doi: 10.1109/TDSC.2021.3063083.

[3] J. Hoefer, M. Stammler, F. Kreß, T. Hotfilter, T. Harbaum and J. Becker, "BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring," 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Didcot, United Kingdom, 2024, pp. 1-6, doi: 10.1109/DFT63277.2024.10753546.

[4] Z. Chen, G. Li and K. Pattabiraman, "A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction," in IEEE Design & Test, doi: 10.1109/MDAT.2025.3618758.

[5] J. Kappes, J. Geier, P. van Kempen, D. Mueller-Gritschneder and U. Schlichtmann, "Automated Graph-level Passes for TinyML Fault Tolerance," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-9, doi: 10.1109/IJCNN64981.2025.11227379.

Voraussetzungen

As we enter the quantum era, traditional encryption is becoming vulnerable. Post-Quantum Cryptography (PQC) is the next-generation standard for securing global data. However, designing hardware for PQC requires specialized tools. Yosys is the world’s leading open-source logic synthesis framework, used to transform hardware descriptions (Verilog) into real circuit structures.

This is a hands-on, exploration-oriented project. You will:

• Master the Tool: Set up and explore the Yosys open-source synthesis framework in a Linux environment.
• Hardware Analysis: Run logic synthesis for PQC-related modules (e.g., Keccak/SHA-3 or Kyber operators) and visualize how they are mapped into gate-level circuits.
• Internal Discovery: Peek "under the hood" of Yosys to understand how it translates high-level code into optimized hardware netlists.
• Documentation: Create a "Technical Internal Guide" that explains Yosys’s key features and workflows to help the lab adopt this tool for future chip designs.

Voraussetzungen

Open-source hardware and EDA tools are increasingly adopted to improve transparency, reproducibility, and technological sovereignty in chip design. RISC-V, as an open instruction set architecture, provides an ideal foundation for building secure and extensible system-on-chip (SoC) platforms. However, implementing complex 64-bit RISC-V SoCs using open-source backend design flows still presents significant challenges in terms of scalability, timing closure, and power efficiency.

This thesis aims to develop and optimize an open-source backend design flow for a secure 64-bit RISC-V SoC. The student will implement a complete backend flow, from synthesized RTL to final layout, using open-source EDA tools. The work will focus on identifying and addressing bottlenecks in the physical design process, such as floorplanning, power distribution, clock tree synthesis, and routing. Security-related design considerations (e.g., integration of security or cryptographic modules) may also be included. The final outcome will be a validated backend flow and a case-study implementation of a secure RISC-V SoC.

Outstanding candidates may be considered for a short-term on-site research stay at TUMCREATE (Singapore), subject to mutual interest and project needs.

Voraussetzungen

Post-quantum cryptography (PQC) is a key enabler for future quantum-safe systems, but the physical realization of PQC hardware modules remains challenging due to their high computational complexity and strict performance and energy constraints. While many PQC algorithms have been studied at the algorithmic and architectural levels, their physical design aspects using open-source EDA tools are still underexplored.

This thesis focuses on the physical implementation and evaluation of PQC hardware modules using an open-source EDA flow. The student will select representative PQC modules (e.g., key computational kernels or full accelerators) and implement them from RTL to layout using open-source tools for synthesis, placement, and routing. Different architectural and implementation configurations will be explored to study their impact on area, timing, and power. The outcome of this work will provide practical insights into the physical design trade-offs of PQC hardware and contribute to open and reproducible chip design methodologies.

Outstanding candidates may be considered for a short-term on-site research stay at TUMCREATE (Singapore), subject to mutual interest and project needs.

Voraussetzungen

As Optical Networks-on-Chip (ONoCs) scale to support an increasing number of cores and diverse communication patterns, a single, uniform router design is often insufficient to achieve optimal performance and energy efficiency. Different communication requirements—such as global data exchange and local traffic—may benefit from different types of optical routers and interconnection structures.

This thesis explores the customized design and composition of optical routers for large-scale ONoC systems. Instead of selecting a single router architecture, the project investigates how different router types (e.g., ring-based routers for global communication and compact routers for local communication) can be combined and deployed to better match application communication patterns. In addition, the thesis will address the task-to-node mapping problem, jointly considering application-level communication behavior and the underlying ONoC structure.

The goal is to develop a co-design framework that integrates router customization, network architecture, and task mapping, enabling more efficient and scalable optical network designs.

Voraussetzungen

Optical Ring Networks-on-Chip (ORNoCs) are widely adopted due to their structural simplicity and scalability. However, the design of ring routers involves tightly coupled decisions between ring waveguide connections and signal path planning, which are often handled separately in existing approaches. This separation can lead to suboptimal designs in terms of performance, power consumption, and physical feasibility.

The objective of this thesis is to develop a design automation framework for ring-based ONoC routers that jointly optimizes ring waveguide connections and signal paths. The problem will be formulated using optimization techniques, with a particular focus on Mixed-Integer Linear Programming (MILP) and algorithmic design. The framework aims to automatically generate efficient and physically feasible router designs under given architectural and physical constraints.

This project is well suited for students interested in optimization, algorithms, and design automation, and provides hands-on experience in translating complex design problems into mathematical models.

Voraussetzungen

Optical Networks-on-Chip (ONoCs) are a promising solution to meet the ever-increasing bandwidth and energy-efficiency demands of many-core systems. However, noise has become a critical challenge in practical ONoC router designs. Such noise originates not only from optical sources (e.g., crosstalk, signal interference, and resonance shift of photonic devices), but also from electrical components, such as power delivery networks (PDN), drivers, and control circuits.

The goal of this thesis is to develop a noise prediction model for ONoC routers that jointly considers optical and electrical noise sources. The proposed model aims to efficiently estimate noise levels at the router level without relying solely on time-consuming full-scale simulations. The prediction results will be quantitatively evaluated and validated by comparing them with existing simulation tools (e.g., Ansys Lumerical).

This project will help bridge the gap between high-level ONoC design and low-level physical effects, and can serve as a foundation for future noise-aware router design and optimization.

Voraussetzungen

One of the crucial tasks in designing, testing, and verifying a digital system is the early estimation of its fault tolerance. For this, fault injection simulations can be used to evaluate this tolerance at different phases of development. At the Register Transfer Level (RTL), an existing but not yet implemented hardware design can be simulated with higher accuracy than its simulation at the instruction or algorithm level. However, accuracy comes at a cost that grows with the number of simulations done within a fault injection analysis. For example, fault injection simulation of a CPU at RTL instead of Instruction Set Architecture (ISA) level would increase the simulation effort to include micro-architectural registers (pipeline, functional units, etc.),

To allow faster fault space exploration at RTL, one could do the following:  (a) accelerate the simulation of an individual Device Under Test (DUT) and fault, or 
  (b) launch multiple fault simulations concurrently

In the case of (a), state-of-the-art research on fast RTL simulations has aimed to reduce simulation cost by multi-threaded simulation on CPUs [1][2][3] or GPUs [4][5][8].

In case (b), multiple independent simulations may be launched simultaneously on a distributed compute cluster. Parallelization of an individual simulation is not as substantial, since the computational platform is utilized anyway, i.e., task-level parallelism of individual simulations for long simulations: one CPU core per fault experiment.

Existing solutions for (b) mainly aim for CPU-based clusters [6][7], whereas for (a), the maximum speed of a single DUT simulation is required. In this work, we want to explore efficient fault simulation at RTL on GPU-based compute clusters that maximizes utilization with the number of individual experiments launched.

Tasks:

1. Set up GPU-accelerated RTL simulation of [8][9]
2. Implement a new fault injection arbitration framework for the RTL simulator
3. Explore multi-experiment partitioning for fault simulation on the new platform
4. Compare and benchmark against a CPU-based fault exploration scheme [6][7]

References:

• [1] W. Snyder, P. Wasson, D. Galbi, et al. Verilator. https://github.com/verilator/verilator, 2019. [Online].
• [2] S. Beamer and D. Donofrio, "Efficiently Exploiting Low Activity Factors to Accelerate RTL Simulation," 2020 57th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2020, pp. 1-6, doi: 10.1109/DAC18072.2020.9218632.
• [3] Kexing Zhou, Yun Liang, Yibo Lin, Runsheng Wang, and Ru Huang. 2023. Khronos: Fusing Memory Access for Improved Hardware RTL Simulation. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '23). Association for Computing Machinery, New York, NY, USA, 180–193. https://doi.org/10.1145/3613424.3614301
• [4] H. Qian and Y. Deng, "Accelerating RTL simulation with GPUs," 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, CA, USA, 2011, pp. 687-693, doi: 10.1109/ICCAD.2011.6105404.
• [5] Dian-Lun Lin, Haoxing Ren, Yanqing Zhang, Brucek Khailany, and Tsung-Wei Huang. 2023. From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus. In Proceedings of the 51st International Conference on Parallel Processing (ICPP '22). Association for Computing Machinery, New York, NY, USA, Article 88, 1–12. https://doi.org/10.1145/3545008.3545091
• [6] Johannes Geier and Daniel Mueller-Gritschneder. 2023. VRTLmod: An LLVM based Open-source Tool to Enable Fault Injection in Verilator RTL Simulations. In Proceedings of the 20th ACM International Conference on Computing Frontiers (CF '23). Association for Computing Machinery, New York, NY, USA, 387–388. https://doi.org/10.1145/3587135.3591435
• [7] J. Geier, L. Kontopoulos, D. Mueller-Gritschneder and U. Schlichtmann, "Rapid Fault Injection Simulation by Hash-Based Differential Fault Effect Equivalence Checks," 2025 Design, Automation & Test in Europe Conference (DATE), Lyon, France, 2025, pp. 1-7, doi: 10.23919/DATE64628.2025.10993266.
• [8] Guo, Zizheng, et al. "GEM: GPU-Accelerated Emulator-Inspired RTL Simulation.https://guozz.cn/publication/gemdac-25/gemdac-25.pdf
• [9] Github NVLabs https://github.com/NVlabs/GEM

Voraussetzungen

Accurate parasitic extraction is essential for standard cell analysis, since parasitic capacitance has a direct impact on delay, power, and overall circuit behavior. Conventional extraction approaches often involve a trade-off between accuracy and runtime: rule-based or 2.5D methods are efficient but may lose accuracy on complex layout patterns, while 3D field-solver-based methods provide high fidelity at significantly higher computational cost. Prior work has shown that machine learning can help bridge this gap by enabling fast yet accurate capacitance prediction for standard cell layouts.

This project explores machine learning methods for standard cell parasitic extraction, with a particular focus on capacitance prediction from layout patterns. We are especially interested in deep-learning-based approaches that can improve both accuracy and efficiency, while still benefiting from physical insight. In particular, physics-guided learning and residual-learning strategies have shown strong promise for reducing prediction error and improving robustness in standard cell capacitance extraction.

A central focus of the project is the development and evaluation of deep neural network models, especially DNN- and ResNet-based approaches, for predicting layout-dependent capacitance. Existing studies suggest that deep models are particularly useful in more complex extraction tasks, where simpler methods may fail to capture the relevant trends. The project is therefore well suited for students who are interested in applying modern deep learning methods to practical problems in circuit and layout analysis.

Voraussetzungen

Robust and Explainable Cas13d Guide RNA Design

RNA-targeting CRISPR systems such as Cas13d have emerged as powerful tools for programmable transcriptome engineering. While STOA frameworks improve practical guide RNA design using sequence-only deep learning models and interpretable interfaces, important challenges remain unresolved. Current systems primarily optimize predicted efficacy, while real-world applications require balancing multiple objectives including specificity, robustness across transcripts, structural accessibility, and uncertainty estimation.

This project aims to develop a next-generation design automation framework that performs multi-objective and uncertainty-aware guide RNA optimization for Cas13d systems. The student will investigate how deep learning architectures can jointly model guide efficacy, off-target risk, transcript accessibility, and confidence estimation using only computationally obtainable information.

The project may include:

• designing improved hybrid architectures (Transformer/CNN/attention-based models),
• integrating RNA secondary structure predictions or accessibility priors,
• developing uncertainty-aware ranking methods,
• implementing multi-objective scoring and candidate ranking,
• creating explainability methods for sequence-level interpretation,
• evaluating generalization across transcripts or datasets,
• and optionally extending the interactive natural-language explanation interface.

The final outcome should be a deployable prototype that produces ranked and interpretable guide RNA candidates from raw transcript sequences while improving robustness and transparency in practical CRISPR RNA-targeting workflows.

Possible Research Questions

• Can uncertainty estimation improve trustworthiness in guide RNA ranking?
• How can off-target risk and efficacy be jointly optimized?
• Which sequence motifs or contextual interactions most strongly determine Cas13d activity?
• Can RNA structure-aware representations improve prediction using only computational inputs?
• How transferable are learned guide design principles across datasets or organisms?

Voraussetzungen

The intersection of artificial intelligence and hardware/system design is rapidly evolving. Many traditional problems in areas such as chip design, optimization, and system architecture are being revisited with modern machine learning techniques—yet there is still vast untapped potential for new ideas.

This open topic is aimed at students who want to go beyond predefined projects and instead explore their own research direction. We are particularly interested in novel and creative approaches, including (but not limited to):

• Applying machine learning to classical hardware or EDA problems
• Reinforcement learning or optimization for system design and scheduling
• AI-driven design space exploration or co-design approaches
• Using modern paradigms such as foundation models or autonomous research/optimization agents
• Completely new ideas that challenge existing workflows or assumptions

The goal is to identify promising research directions and develop them into meaningful projects, with the potential to grow into a thesis or even a research publication.

You will work closely with supervision to refine your idea, scope the problem, and develop a concrete research plan—but the starting point should come from you.

Voraussetzungen

Modern chip design faces increasingly complex optimization challenges, where millions of design decisions must be made under tight constraints. One key problem in this space is macro placement: arranging large components (e.g., SRAM blocks, IPs) on a chip such that routing congestion, timing, power delivery, and area are jointly optimized.

This problem is inherently difficult: it involves a highly discrete design space, multiple competing objectives, and strong global dependencies between decisions. Classical methods have been refined for decades, yet recent advances in machine learning—particularly reinforcement learning and graph-based methods—suggest new opportunities for improvement.

In this research internship, you will work on developing and evaluating novel approaches for macro placement and related optimization problems. The focus is on designing efficient algorithms that can handle large-scale, highly constrained systems and produce high-quality solutions under realistic runtime constraints.

Possible directions include:

• learning-based approaches (e.g., reinforcement learning, GNNs),
• hybrid optimization methods combining heuristics and ML,
• scalable search and approximation techniques,
• or improving classical placement strategies with modern tooling.

As part of the project, there is the opportunity to evaluate your approach on a current industry-backed challenge:

https://partcl.com/blog/macro_placement_challenge

(Submissions deadline: May 21, 2026)

While participation in the challenge is optional, it provides a concrete benchmark and external validation for your work. Projects may be conducted in small supervised groups, but individual contributions and reports are required.

The topic is well suited for continuation into a larger thesis project in the area of machine learning for systems and chip design.

Voraussetzungen

Recent advances in AI/ML models (e.g., large language models) demand unprecedented compute and memory resources, making efficient system design a central challenge. New approaches, such as energy-aware co-optimization of hardware architectures and workload execution, enable significant improvements in energy-delay efficiency and scalability .

This thesis builds on such optimization-driven frameworks and opens up a range of possible research directions. Rather than prescribing a fixed path, the goal is to let you explore and define your own contribution within this space, depending on your interests.

Possible directions include (but are not limited to):

• Optimization & Algorithms
  • Improve scalability and efficiency of optimization solvers (e.g., MIQP-based approaches)
  • Develop approximation, heuristic, or hybrid optimization techniques
  • Explore alternative formulations for large-scale design space exploration
• AI for Systems / Learning-Based Methods
  • Investigate reinforcement learning or learning-based approaches for scheduling, mapping, or architecture design
  • Compare learned vs. analytical optimization strategies
• Scalable Systems & Workloads
  • Extend analyses to extremely large workloads (e.g., LLM-scale systems)
  • Study trade-offs between performance, energy, and hardware constraints
• Hardware & Architecture Exploration
  • Analyze emerging architectures such as multi-chiplet systems
  • Explore memory hierarchies, interconnects, and power management strategies
• Tooling & Visualization
  • Develop intuitive interfaces or visual analytics tools for design space exploration
  • Make complex optimization results interpretable and interactive

The work can be adapted toward a more theoretical, systems-oriented, or practical/software-driven thesis. We aim to produce publishable research results, making this an excellent opportunity for students interested in academia or advanced R&D.

Voraussetzungen

Emerging computing architectures increasingly rely on chiplet-based systems and distributed execution to efficiently run complex workloads such as AI inference. In such systems, computations and intermediate results are exchanged between multiple processing units. Ensuring the integrity and correctness of these computations becomes an important challenge, particularly in the presence of hardware faults, communication errors, or malicious manipulation.

Techniques for detecting computational errors have long been studied, for example, through Algorithm-based Fault Tolerance (ABFT) methods for linear algebra operations [1]. More recently, similar concepts have been explored for machine learning workloads and NN inference, where protecting intermediate results and detecting corrupted computations is becoming increasingly important [2], [3], [4], [5]. At the same time, emerging architectures such as chiplet-based systems introduce new challenges for ensuring reliable execution across distributed compute units.

This student project investigates mechanisms for verifying the correctness of distributed AI computations in heterogeneous and chiplet-based architectures. Possible directions include:

• Techniques for integrity verification of distributed AI inference

• Detection of faults or corrupted intermediate results

• Lightweight verification mechanisms based on algorithmic or system-level approaches

• Analysis of trade-offs between reliability, performance, and overhead

References

[1] Kuang-Hua Huang and J. A. Abraham, "Algorithm-Based Fault Tolerance for Matrix Operations," in IEEE Transactions on Computers, vol. C-33, no. 6, pp. 518-528, June 1984, doi: 10.1109/TC.1984.1676475.

[2] S. K. S. Hari, M. B. Sullivan, T. Tsai and S. W. Keckler, "Making Convolutions Resilient Via Algorithm-Based Error Detection Techniques," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 4, pp. 2546-2558, 1 July-Aug. 2022, doi: 10.1109/TDSC.2021.3063083.

[3] J. Hoefer, M. Stammler, F. Kreß, T. Hotfilter, T. Harbaum and J. Becker, "BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring," 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Didcot, United Kingdom, 2024, pp. 1-6, doi: 10.1109/DFT63277.2024.10753546.

[4] Z. Chen, G. Li and K. Pattabiraman, "A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction," in IEEE Design & Test, doi: 10.1109/MDAT.2025.3618758.

[5] J. Kappes, J. Geier, P. van Kempen, D. Mueller-Gritschneder and U. Schlichtmann, "Automated Graph-level Passes for TinyML Fault Tolerance," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-9, doi: 10.1109/IJCNN64981.2025.11227379.

Voraussetzungen

As we enter the quantum era, traditional encryption is becoming vulnerable. Post-Quantum Cryptography (PQC) is the next-generation standard for securing global data. However, designing hardware for PQC requires specialized tools. Yosys is the world’s leading open-source logic synthesis framework, used to transform hardware descriptions (Verilog) into real circuit structures.

This is a hands-on, exploration-oriented project. You will:

• Master the Tool: Set up and explore the Yosys open-source synthesis framework in a Linux environment.
• Hardware Analysis: Run logic synthesis for PQC-related modules (e.g., Keccak/SHA-3 or Kyber operators) and visualize how they are mapped into gate-level circuits.
• Internal Discovery: Peek "under the hood" of Yosys to understand how it translates high-level code into optimized hardware netlists.
• Documentation: Create a "Technical Internal Guide" that explains Yosys’s key features and workflows to help the lab adopt this tool for future chip designs.

Voraussetzungen

Robust and Explainable Cas13d Guide RNA Design

RNA-targeting CRISPR systems such as Cas13d have emerged as powerful tools for programmable transcriptome engineering. While STOA frameworks improve practical guide RNA design using sequence-only deep learning models and interpretable interfaces, important challenges remain unresolved. Current systems primarily optimize predicted efficacy, while real-world applications require balancing multiple objectives including specificity, robustness across transcripts, structural accessibility, and uncertainty estimation.

This project aims to develop a next-generation design automation framework that performs multi-objective and uncertainty-aware guide RNA optimization for Cas13d systems. The student will investigate how deep learning architectures can jointly model guide efficacy, off-target risk, transcript accessibility, and confidence estimation using only computationally obtainable information.

The project may include:

• designing improved hybrid architectures (Transformer/CNN/attention-based models),
• integrating RNA secondary structure predictions or accessibility priors,
• developing uncertainty-aware ranking methods,
• implementing multi-objective scoring and candidate ranking,
• creating explainability methods for sequence-level interpretation,
• evaluating generalization across transcripts or datasets,
• and optionally extending the interactive natural-language explanation interface.

The final outcome should be a deployable prototype that produces ranked and interpretable guide RNA candidates from raw transcript sequences while improving robustness and transparency in practical CRISPR RNA-targeting workflows.

Possible Research Questions

• Can uncertainty estimation improve trustworthiness in guide RNA ranking?
• How can off-target risk and efficacy be jointly optimized?
• Which sequence motifs or contextual interactions most strongly determine Cas13d activity?
• Can RNA structure-aware representations improve prediction using only computational inputs?
• How transferable are learned guide design principles across datasets or organisms?

Voraussetzungen

The intersection of artificial intelligence and hardware/system design is rapidly evolving. Many traditional problems in areas such as chip design, optimization, and system architecture are being revisited with modern machine learning techniques—yet there is still vast untapped potential for new ideas.

This open topic is aimed at students who want to go beyond predefined projects and instead explore their own research direction. We are particularly interested in novel and creative approaches, including (but not limited to):

• Applying machine learning to classical hardware or EDA problems
• Reinforcement learning or optimization for system design and scheduling
• AI-driven design space exploration or co-design approaches
• Using modern paradigms such as foundation models or autonomous research/optimization agents
• Completely new ideas that challenge existing workflows or assumptions

The goal is to identify promising research directions and develop them into meaningful projects, with the potential to grow into a thesis or even a research publication.

You will work closely with supervision to refine your idea, scope the problem, and develop a concrete research plan—but the starting point should come from you.

Voraussetzungen

Large language models continue to push modern hardware systems to their limits. In particular, data movement, memory access costs, and energy efficiency have become central bottlenecks for scalable inference. Compute-in-memory architectures offer a promising direction to address these challenges by bringing computation closer to where data resides and thereby reducing the overhead of conventional processor-centric systems.

In this research internship, you will contribute to an ongoing project on compute-in-memory architectures for LLM inference. The focus will be on deepening and sharpening the current research by analyzing recent developments, strengthening technical comparisons, refining system-level insights, and helping consolidate the latest progress in this rapidly evolving area.

This may include:

• studying recent work on efficient LLM inference and compute-in-memory systems,
• comparing architectural approaches and identifying key design trade-offs,
• helping structure and sharpen technical arguments and insights,
• supporting the analysis of hardware, performance, and energy-efficiency aspects,
• and contributing to the further development of a research manuscript.

The internship is particularly well suited for students who are interested in academia and would like to gain hands-on experience in how research is developed and turned into a strong scientific outcome. You will work on a highly relevant topic with clear practical and research impact, and gain insight into current questions in next-generation AI hardware.

Voraussetzungen

Recent advances in AI/ML models (e.g., large language models) demand unprecedented compute and memory resources, making efficient system design a central challenge. New approaches, such as energy-aware co-optimization of hardware architectures and workload execution, enable significant improvements in energy-delay efficiency and scalability .

This thesis builds on such optimization-driven frameworks and opens up a range of possible research directions. Rather than prescribing a fixed path, the goal is to let you explore and define your own contribution within this space, depending on your interests.

Possible directions include (but are not limited to):

• Optimization & Algorithms
  • Improve scalability and efficiency of optimization solvers (e.g., MIQP-based approaches)
  • Develop approximation, heuristic, or hybrid optimization techniques
  • Explore alternative formulations for large-scale design space exploration
• AI for Systems / Learning-Based Methods
  • Investigate reinforcement learning or learning-based approaches for scheduling, mapping, or architecture design
  • Compare learned vs. analytical optimization strategies
• Scalable Systems & Workloads
  • Extend analyses to extremely large workloads (e.g., LLM-scale systems)
  • Study trade-offs between performance, energy, and hardware constraints
• Hardware & Architecture Exploration
  • Analyze emerging architectures such as multi-chiplet systems
  • Explore memory hierarchies, interconnects, and power management strategies
• Tooling & Visualization
  • Develop intuitive interfaces or visual analytics tools for design space exploration
  • Make complex optimization results interpretable and interactive

The work can be adapted toward a more theoretical, systems-oriented, or practical/software-driven thesis. We aim to produce publishable research results, making this an excellent opportunity for students interested in academia or advanced R&D.

Voraussetzungen

About us

We, the Chair of Electronic Design Automation at the School of Computation, Information and Technology of TUM, are developing a new web-based learning and practice platform to help students deepen their understanding of the core topics of our Discrete Mathematics for Engineers (DMI) course. Our plans for the platform include:

·        Automatically generated exercise and exam?style tasks (e.g., Resolution Method, Warshall-Algorithm)

·        Automatic checking of solutions or the generation of sample solutions

·        Live in?lecture questions for activation and quick knowledge assessment, including visualization of the results for the lecturer.

Beyond the core platform, we aim to explore and integrate machine?learning or generative AI methods to enable or improve:

·        Detection and explanation of typical student errors

·        Difficulty estimation of practice problems based on task features and student feedback

·        Generation of practice problems that might be hard to generate programmatically, in conjunction with strict sanity-checking to ensure correctness of generated tasks and sample solutions

Your role focuses on developing and evaluating AI?based components and integrating them into the platform, which will be built simultaneously by another student.

Your Tasks

·        Explore suitable generative AI methods, e.g., for task creation (with correctness safeguards)

·        Implement systems to detect typical student errors using ML techniques

·        Prototype AI?based correctness checks for more complex tasks

·        Design a system to collect and evaluate student difficulty ratings

·        Develop an ML model that predicts task difficulty based on structural task features

·        Collaborate closely with another student assistant working on the underlying learning platform

·        Document experiments, methodologies, and findings for future extensions

We offer

·        A fixed-term “studentische Hilfskraft” position of 84 hours over the course of the summer semester 2026 (14 * 6h/week preferred, other arrangements can be negotiated)

·        The work can be carried out in-person or remotely

·        TUM aims to increase the proportion of women; therefore, applications from women are expressly welcomed.

·        The position is suitable for persons with severe disabilities. Applicants with severe disabilities will be given preference in hiring when they have essentially the same qualifications, aptitude, and professional performance as other candidates.

·        In the context of your application for a position at the Technical University of Munich (TUM), you are transmitting personal data. Please take note of our data protection information pursuant to Article 13 of the General Data Protection Regulation (GDPR) regarding the collection and processing of personal data as part of your application: https://portal.mytum.de/kompass/datenschutz/Bewerbung/
By submitting your application, you confirm that you have taken note of TUM’s data protection information.

Application

We look forward to your application. Please include your CV and Transcript of Records in an email to
benedikt.schaible@tum.de

Voraussetzungen

About us

We, the Chair of Electronic Design Automation at the School of Computation, Information and Technology of TUM, are developing a new web-based learning and practice platform to help students deepen their understanding of the core topics of our Discrete Mathematics for Engineers (DMI) course. Our plans for the platform include:

·        Automatically generated exercise and exam?style tasks (e.g., Resolution Method, Warshall-Algorithm)

·        Automatic checking of solutions or the generation of sample solutions

·        Live in?lecture questions for activation and quick knowledge assessment, including visualization of the results for the lecturer.

Your work will focus on building the core system, ensuring an extensible and scalable architecture that will later enable seamless integration of additional exercise types and features, possibly including generative AI components.

Your Tasks

·        Evaluate and set up a suitable framework to build the web application on

·        Ensure modularity and extensibility for future task types

·        Develop interactive task modules (both based on existing solvers, as well as new developments)

·        Build intuitive solution-entry methods that allow for automatic checking of students' solutions

·        Build an interface for live?quiz functionalities and lecturer dashboard

We offer

·        A fixed-term “studentische Hilfskraft” position of 140 hours over the course of the summer semester 2026 (14 * 10h/week preferred, other arrangements can be negotiated)

·        If desired, a follow-up contract for 90 hours over the course of the winter semester 2026/27 will be available

·        The work can be carried out in-person or remotely

·        TUM aims to increase the proportion of women; therefore, applications from women are expressly welcomed.

·        The position is suitable for persons with severe disabilities. Applicants with severe disabilities will be given preference in hiring when they have essentially the same qualifications, aptitude, and professional performance as other candidates.

·        In the context of your application for a position at the Technical University of Munich (TUM), you are transmitting personal data. Please take note of our data protection information pursuant to Article 13 of the General Data Protection Regulation (GDPR) regarding the collection and processing of personal data as part of your application: https://portal.mytum.de/kompass/datenschutz/Bewerbung/
By submitting your application, you confirm that you have taken note of TUM’s data protection information.

Application

We look forward to your application. Please include your CV and Transcript of Records in an email to
benedikt.schaible@tum.de

Voraussetzungen

Matrix extensions are a key building block for accelerating modern workloads such as machine learning, signal processing, and data analytics. There are tree styles of matrix extensions in development by the RISC-V community:

• Vector-Matrix-Extension (VME)

• Integrated-Matrix-Extension (IME)

• Attached-Matrix-Extension (AME)

In this project, the student will model the proposed IME instructions at the ISA level and integrate them into the ETISS instruction set simulator using the CoreDSL2 ecosystem. This includes defining instruction encodings, assembler syntax, and functional behavior, as well as developing a host-side simulation library to emulate matrix operations.

The extended simulator will be used to evaluate the IME design through testing and benchmarking, allowing analysis of correctness, usability, and performance characteristics. The project provides hands-on experience with ISA design, virtual prototyping, and simulation-based evaluation of emerging hardware extensions, closely aligned with current industrial and academic research.

Tasks

• Study the proposed RISC-V IME specification and analyze differences compared to VME and AME approaches
• ISA-level modeling of IME instructions using CoreDSL2 (instruction encoding, assembler syntax, and semantic behavior)
• Development of a host-side simulation library (based on existing softvector libraries) to emulate matrix operations
• Integration of IME support into the ETISS simulator using the CoreDSL retargeting ecosystem
• Evaluation of the IME extension through testing, benchmarking, and example workloads

Reading Material

• IME Proposal (SpaceMiT): https://github.com/spacemit-com/riscv-ime-extension-spec

• Matrix Extension Proposal (T-Head): https://github.com/XUANTIE-RV/riscv-matrix-extension-spec?tab=readme-ov-file

• IME Mailing List: https://lists.riscv.org/g/tech-integrated-matrix-extension

• IME Charter: https://riscv.atlassian.net/wiki/spaces/IMEX/pages/46071925/Charter
• VME Mailing List:

• VME Charter: https://riscv.atlassian.net/wiki/spaces/VMEX/pages/663912452/Vector-Matrix+Extension+VME+Charter

•  AME Mailing List: https://lists.riscv.org/g/tech-attached-matrix-extension

• AME Charter: https://riscv.atlassian.net/wiki/spaces/AMEX/pages/55083388/Charter

Voraussetzungen

Optical Networks-on-Chip (ONoCs) are a promising solution to meet the ever-increasing bandwidth and energy-efficiency demands of many-core systems. However, noise has become a critical challenge in practical ONoC router designs. Such noise originates not only from optical sources (e.g., crosstalk, signal interference, and resonance shift of photonic devices), but also from electrical components, such as power delivery networks (PDN), drivers, and control circuits.

The goal of this thesis is to develop a noise prediction model for ONoC routers that jointly considers optical and electrical noise sources. The proposed model aims to efficiently estimate noise levels at the router level without relying solely on time-consuming full-scale simulations. The prediction results will be quantitatively evaluated and validated by comparing them with existing simulation tools (e.g., Ansys Lumerical).

This project will help bridge the gap between high-level ONoC design and low-level physical effects, and can serve as a foundation for future noise-aware router design and optimization.

Voraussetzungen