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.

翻译：本研究提出了包括卷积神经网络（CNN）、循环神经网络（RNN）、长短期记忆网络（LSTM）和深度信念网络（DBN）在内的先进神经网络架构，用于利用现场可编程门阵列（FPGA）增强心电图信号分析。我们使用MIT-BIH心律失常数据库进行训练与验证，并通过引入高斯噪声提升算法鲁棒性。所实现的模型包含多个层以完成不同的处理与分类任务，并采用EarlyStopping回调函数和Dropout层等技术缓解过拟合。本工作还探索了为PYNQ Z1板定制张量计算单元（TCU）加速器的开发，提供了基于FPGA的机器学习完整步骤：包括在Docker中配置Tensil工具链、选择架构、设置PS-PL接口、编译及执行模型。通过计算延迟和吞吐量等性能指标获得实际洞察，展示了FPGA在高性能生物医学计算中的潜力。本研究最终为优化FPGA上神经网络性能以应对多种应用提供了指导。