Exploiting FPGA Capabilities for Accelerated Biomedical Computing
Kayode Inadagbo Baran Arig Nisanur Alici Murat Isik
This study presents advanced neural network architectures including
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long
Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for
enhanced ECG signal analysis using Field Programmable Gate Arrays (FPGAs). We
utilize the MIT-BIH Arrhythmia Database for training and validation,
introducing Gaussian noise to improve algorithm robustness. The implemented
models feature various layers for distinct processing and classification tasks
and techniques like EarlyStopping callback and Dropout layer are used to
mitigate overfitting. Our work also explores the development of a custom Tensor
Compute Unit (TCU) accelerator for the PYNQ Z1 board, offering comprehensive
steps for FPGA-based machine learning, including setting up the Tensil
toolchain in Docker, selecting architecture, configuring PS-PL, and compiling
and executing models. Performance metrics such as latency and throughput are
calculated for practical insights, demonstrating the potential of FPGAs in
high-performance biomedical computing. The study ultimately offers a guide for
optimizing neural network performance on FPGAs for various applications.