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.

本文介绍了各种技术如何被用于发展高效的深度学习加速器以解决医疗保健中的医学诊断、模式识别和信号处理等问题，同时探讨脉冲神经形态处理器如何拓展更多医学信号。最终，该文为读者提供了关于医疗保健和生物医学领域中不同加速器和神经形态处理器的优缺点、挑战和机遇的分析。