The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.

中文翻译：

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面向医疗保健和生物医学应用的深度网络加速器的硬件实现

专用深度学习 (DL) 加速器和神经形态处理器的出现为将深度和尖峰神经网络 (SNN) 算法应用于边缘的医疗保健和生物医学应用带来了新的机会。这可以促进医疗物联网 (IoT) 系统和护理点 (PoC) 设备的发展。在本文中，我们提供了一个教程，介绍了如何使用各种技术（包括新兴的忆阻器件、现场可编程门阵列 (FPGA) 和互补金属氧化物半导体 (CMOS)）来开发高效的 DL 加速器，以解决各种诊断、模式问题。医疗保健中的识别和信号处理问题。此外，我们探索了尖峰神经形态处理器如何补充其 DL 对应物以处理生物医学信号。本教程补充了大量关于神经网络和神经形态硬件应用于医疗保健领域的文献的案例研究。我们通过执行将肌电图 (EMG) 信号与计算机视觉相结合的传感器融合信号处理任务来对各种硬件平台进行基准测试。在推理延迟和能量方面对专用神经形态处理器和嵌入式 AI 加速器进行了比较。最后，我们提供了对该领域的分析，并分享了对各种加速器和神经形态处理器引入医疗保健和生物医学领域的优势、劣势、挑战和机遇的看法。我们通过执行将肌电图 (EMG) 信号与计算机视觉相结合的传感器融合信号处理任务来对各种硬件平台进行基准测试。在推理延迟和能量方面对专用神经形态处理器和嵌入式 AI 加速器进行了比较。最后，我们提供了对该领域的分析，并分享了对各种加速器和神经形态处理器引入医疗保健和生物医学领域的优势、劣势、挑战和机遇的看法。我们通过执行将肌电图 (EMG) 信号与计算机视觉相结合的传感器融合信号处理任务来对各种硬件平台进行基准测试。在推理延迟和能量方面对专用神经形态处理器和嵌入式 AI 加速器进行了比较。最后，我们提供了对该领域的分析，并分享了对各种加速器和神经形态处理器引入医疗保健和生物医学领域的优势、劣势、挑战和机遇的看法。