Jonas Kantic, M.Sc.
Research Associate
Technische Universität München
Department of Electrical and Computer Engineering
Chair of Integrated Systems
Arcisstr. 21
80333 München
Germany
Tel.: +49.89.289.22962
Fax: +49.89.289.28323
Gebäude: N1 (Theresienstr. 90)
Raum: N2118
Email: jonas.kantic@tum.de
Curriculum Vitae
Education
- 2017 - 2020 Master's Studies in Technical Informatics, Leibniz University Hannover
- 2018 - 2019 Chinese Language, Beijing Foreign Studies Univeristy, Beijing
- 2013 - 2017 Bachelor's Studies in Technical Informatics, Leibniz University Hannover
Work Experience
- Since 2022 PhD student at the Chair of Integrated Systems, Technical University of Munich
- 2021 Regular Research Assistant, Institute of Microelectronic Systems, Leibniz University Hannover
- 2019 - 2020 Internship: BMW China Services, Beijing
- 2014 - 2020 Student Research Assistant, Institute of Microelectronic Systems, Leibniz University Hannover
Open Student Work
Current Student Work
FPGA Implementations of RNNs: A Survey
Description
Field-programmable gate array (FPGA) implementations of recurrent neural networks (RNNs) are crucial because they provide high performance with low power consumption, making them ideal for real-time applications and embedded systems. Recent advances have shown that FPGAs can outperform traditional platforms like GPUs regarding energy efficiency while maintaining comparable accuracy.
In this seminar topic, your task is to introduce and summarize recent approaches for FPGA-based RNN accelerators. Furthermore, a comparison of different implementations concerning resource usage (lookup tables (LUTs), registers, digital signal processors (DSPs), and power dissipation) and performance (predictions per second, real-time capability) should be composed.
Outline:
- Literature Review: Get an overview of recent advances in FPGA implementations of RNNs
- Comparative Analysis: Summarize and compare the concepts of the most important implementations concerning resource usage and performance
- Scientific Writing: Compose your findings in a paper, resulting in a concise overview and comparison
- Presentation: Present your findings to other members of the seminar.
Prerequisites
- Be familiar with deep learning, especially recurrent neural network architectures
- Be familiar with FPGAs
Supervisor:
Enhancement of Vehicle Control Systems using Time-Series-Prediction
Time Series Prediction, Machine Learning, Neural Networks
Description
Summary:
Current vehicle dynamics control systems regulate various vehicle state variables using classic PID control methods by comparing desired and actual states. The quality of such a controller can only be improved to a limited extent through parameter optimization, as the control is based solely on measured actual states. A conventional approach to solving this problem involves using a highly complex physical model to predict the future behavior of a signal based on known input parameters, thereby improving controller performance.
However, as such a model far exceeds the hardware limitations of a vehicle control unit, an alternative solution is to make predictions using a machine learning model. This research aims to investigate the feasibility and quality of such machine learning predictions and the resulting control loop quality using the example of motorcycle traction control.
Methodology:
The proposed methodology involves developing a time-series prediction approach,
potentially utilizing sequence-to-sequence classification, e.g., to determine the road
surface, tire types, loading conditions and other parameters as input for time series
prediction. To achieve this, various suitable model architectures (e.g., LSTM, GRU,
Transformer, Reservoir Computing) will be identified in the literature, and appropriate signals and datasets will be selected from existing vehicle data. The models will then be verified as open-loop in simulation, and the most suitable method and relevant data will be identified. If the simulation results are positive, the model will be implemented in a real-time hardware environment to test closed-loop performance.
Research Questions:
- Is predicting sensor signals possible using time-series prediction in an open-loop system?
- What data and model are necessary to enable robust prediction?
- Is control based on prediction possible in a closed-loop system?
Contact
Email: Florian.huelsmann@bmw.de
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Completed Student Work
Contact
frieder.jespers@nxp.com
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Jonas Kantic | Room: N2118 | Tel: +49.89.289.22962 | E-Mail: jonas.kantic@tum.de
Supervisor:
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Jonas Kantic
Chair of Integrated Systems
Office N2118, Building N1
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Email: fabian.legl@ifta.com
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Publications
- ReLiCADA: Reservoir Computing Using Linear Cellular Automata design algorithm. Complex & Intelligent Systems, 2024 more…
Publications (Pre-TUM)
Journals
Klein, Simon Christian, Kantic, Jonas and Blume, Holger. "Fixed Point Analysis Workflow for efficient Design of Convolutional Neural Networks in Hearing Aids" in Current Directions in Biomedical Engineering, vol. 7, no. 2, 2021, pp. 787-790, DOI: https://doi.org/10.1515/cdbme-2021-2201.