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.

翻译：本研究深入探究了卷积神经网络(CNN)、循环神经网络(RNN)、长短期记忆网络(LSTM)及深度信念网络(DBN)等先进神经网络框架，旨在通过现场可编程门阵列(FPGA)提升心电图(ECG)信号的分析能力。研究以MIT-BIH心律失常数据库为基础进行模型训练与评估，并引入高斯噪声以增强算法的鲁棒性。所构建的架构包含多种专用处理与分类功能层，采用EarlyStopping回调函数和Dropout层等策略防止过拟合。此外，本文详细阐述了面向PYNQ Z1平台定制的张量计算单元(TCU)加速器设计方法，提供了基于FPGA的机器学习完整实施流程，涵盖Docker中Tensil工具链配置、架构选择、PS-PL配置及模型编译部署等环节。通过评估延迟和吞吐量等性能指标，我们验证了FPGA在先进生物医学计算中的有效性。最终，本研究为各领域在FPGA上优化神经网络运算提供了全面指南。