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.

本研究详细探讨了使用卷积神经网络（CNN）、循环神经网络（RNN）、长短期记忆网络（LSTM）和深度置信网络（DBN）通过可编程门阵列（FPGA）改进对心电图（ECG）信号的分析的复杂神经网络框架，使用 MIT-BIH 心律失常数据库作为模型训练和评估的基础，并加入高斯噪声来提高算法的鲁棒性。研究中我们采用多层结构进行特定处理和分类功能的设计，使用了 EarlyStopping 回调和 Dropout 层等策略来防止过拟合现象。此外，还详细介绍了为 PYNQ Z1 平台创建定制化的张量计算单元（TCU）加速器的过程。该研究提供了一个系统的方法论，涵盖了在 Docker 中配置 Tensil 工具链、选择架构、PS-PL 配置以及模型的编译和部署。通过评估延迟和吞吐量等性能指标，展示了 FPGA 在先进的生物医学计算中的效果。最终，本研究成果在各个领域优化 FPGA 上的神经网络操作提供了全面的指南。