# Exploiting FPGA Capabilities for Accelerated Biomedical Computing

arXiv: https://arxiv.org/abs/2307.07914 · HTML: https://mlsanity.com/paper/2307.07914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Nengo and low-power AI hardware for robust, embedded neurorobotics](https://mlsanity.com/paper/2007.10227/) (2020)
- [An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA](https://mlsanity.com/paper/2301.07050/) (2023)
- [A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks](https://mlsanity.com/paper/2204.02942/) (2022)
- [Design optimization for high-performance computing using FPGA](https://mlsanity.com/paper/2304.12474/) (2023)
- [RNNAccel: A Fusion Recurrent Neural Network Accelerator for Edge Intelligence](https://mlsanity.com/paper/2010.13311/) (2020)

## Built on by (most cited first, 2 of 2)

- [NEUROSEC: FPGA-Based Neuromorphic Audio Security](https://mlsanity.com/paper/2401.12055/) (2024)
- [Harnessing FPGA Technology for Enhanced Biomedical Computation](https://mlsanity.com/paper/2311.12439/) (2023)

## Abstract (arXiv)

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.