  This research delves into sophisticated neural network frameworks like
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long
Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for
improved analysis of ECG signals via Field Programmable Gate Arrays (FPGAs).
The MIT-BIH Arrhythmia Database serves as the foundation for training and
evaluating our models, with added Gaussian noise to heighten the algorithms'
resilience. The developed architectures incorporate various layers for specific
processing and categorization functions, employing strategies such as the
EarlyStopping callback and Dropout layer to prevent overfitting. Additionally,
this paper details the creation of a tailored Tensor Compute Unit (TCU)
accelerator for the PYNQ Z1 platform. It provides a thorough methodology for
implementing FPGA-based machine learning, encompassing the configuration of the
Tensil toolchain in Docker, selection of architectures, PS-PL configuration,
and the compilation and deployment of models. By evaluating performance
indicators like latency and throughput, we showcase the efficacy of FPGAs in
advanced biomedical computing. This study ultimately serves as a comprehensive
guide to optimizing neural network operations on FPGAs across various fields.
