Description
Neural networks (NNs) have seen great improvements over the last decades and have consequently been adopted for a multitude of applications. While much more capable in certain areas than prior solutions, NNs have one big drawback.

A neural network requires much more power than traditional computational models, making them generally unsuited for embedded devices. The rapid adoption also poses challenges for high performance models, as the amount of processing power required for widespread use strains the existing power grid - with construction of AI data-centers significantly outpacing construction of new power plants. Clearly this growth is unsustainable unless these challenges are addressed.

In part to address these issues, research has been ongoing into techniques which may avoid the high computational cost and power dissipation of standard neural networks, such as Convolutional Neural Networks (CNNs). Particularly for event driven computation, models such as Spiking Neural Networks (SNNs) and/or asynchronous neural networks offer potentially significant benefits; as event driven applications only require that computation is performed once a new event occurs, power can be saved by only being active when a computation is required. Asynchronous circuits take this idea to the extreme by completely avoiding all dynamic power dissipation except when subcircuits have valid inputs available.

Task
For this seminar topic, the student is expected to look into the state-of-the-art for asynchronous neural networks and provide a summary of relevant research. Papers that could serve as potential starting points can be seen below, but the student is free to pursue the topic as they want, within the confines of the scope given in this description. 

Starting points

• A 28nm Configurable Asynchronous SNN Accelerator with Energy-Efficient Learning
• DYNAP-SE2: a scalable multi-core dynamic neuromorphic
  asynchronous spiking neural network processor
• Design and Tool Flow of a Reconfigurable Asynchronous
  Neural Network Accelerator
• A 2048-Neuron Spiking Neural Network
  Accelerator with Neuro-Inspired Pruning and
  Asynchronous Network on Chip in 40nm CMOS

The Parallel Ultra-Low Power (PULP) platform is an open-source hardware and software ecosystem developed by ETH Zürich and the University of Bologna to explore energy-efficient computing. It provides scalable multi-core architectures, SoC components, and toolchains designed for applications where power consumption is critical, such as edge AI, IoT, and embedded sensing.

At its core, PULP focuses on parallelism and low-power techniques, combining lightweight RISC-V processors, tightly coupled memory hierarchies, and domain-specific accelerators. The modular and flexible platform enables researchers and developers to prototype custom system-on-chip designs while leveraging a growing suite of open-source IP blocks, including the CVA6 RISC-V core.

This CPU core, formerly Ariane, is a 64-bit, in-order, six-stage-pipelined application-class processor compatible with the RISC-V RV64GC instruction set. Though optimized for energy efficiency, it is powerful enough to boot operating systems such as Linux or FreeRTOS. With standard interfaces like AXI for memory and peripheral access, a rich toolchain, and a release under the permissive Solderpad license, the CVA6 core is very useful for system-on-chip integration in research.

In this seminar topic, the student should further investigate the PULP platform, its contributions, including the CVA6 core, and especially its recent projects on novel system architectures. Examples for case studies are the lightweight Cheshire and coherence-focused Culsans platforms, or the Occamy chiplet system comprising Snitch clusters. Besides the conceptual aspects, their performance, resource utilization, and tapeout characteristics should be analyzed. Another focus of this seminar should be the toolchains provided by the PULP platform and the workflow of integrating, adapting, and verifying their designs in other projects.

Possible starting points for literature research are listed below.

https://pulp-platform.org/index.html
https://pulp-platform.org/docs/Ariane_detailed.pdf
https://ieeexplore.ieee.org/abstract/document/8777130
https://ieeexplore.ieee.org/abstract/document/10163410
https://arxiv.org/abs/2407.19895
https://ieeexplore.ieee.org/abstract/document/10631529

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Prerequisites

Prefetching is a widely used technique in modern processors to mitigate the memory wall problem by fetching data into faster memory levels before it is actually needed. While prefetching has been extensively studied and deployed in CPUs, where hierarchical cache designs (L1, L2, and off-chip DRAM) dominate, GPUs present a very different challenge.

GPUs are now central to diverse applications ranging from artificial intelligence to scientific computing. Their massively parallel architecture and SIMT execution model create distinct memory access behaviors compared to CPUs. Consequently, conventional CPU prefetching mechanisms are often ineffective or even harmful when applied to GPUs. This has led to the development of GPU-specific prefetching strategies that account for the unique architectural features and execution patterns of GPUs.

The goal of this seminar is to study and compare different GPU prefetching mechanisms. By reviewing recent research papers, participants will gain an understanding of how these mechanisms work, their advantages and limitations, and under what conditions they can improve GPU performance.

Prerequisites

Sporadic anomalies in automotive systems can degrade performance over time and may originate from various system components. In automotive applications, anomalies are often observed at the sensor and ECU levels, with potential propagation through the in-vehicle network via Ethernet. Such anomalies may be the result of deviations in electronic control units, highlighting the importance of monitoring these signals over Ethernet.

Not all processing anomalies are equally detectable over Ethernet due to inherent limitations in the monitoring techniques and the nature of the anomalies. This seminar will explore various anomaly categories, investigate their potential causes, and assess the likelihood of their propagation through the network.

The goal of this seminar is to provide a comprehensive analysis of these anomaly categories, evaluate the underlying causes, and discuss the potential for their detection and mitigation when monitored over Ethernet.

In today’s data-driven world, processing approaches are typically divided between cloud-based solutions—with virtually unlimited resources—and localized processing, which is constrained by hardware limitations. While the cloud offers extensive computational power, localized processing is often required for real-time applications where latency and data security are critical concerns.

To bridge this gap, various algorithms have been developed to pre-process data or extract essential information before it is sent to the cloud.

The goal of this seminar is to explore and compare these algorithms, evaluating their computational load on local hardware and their overall impact on system performance.