Jonas Kantic, M.Sc.

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

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?