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.
翻译:专门的深层学习加速器和神经形态处理器的出现,为在边缘应用深层和Spiking神经网络(SNN)算法对医疗和生物医学应用带来了新的机会。这可以促进物质(IOT)系统和护理点(POC)设备医学互联网的进步。在本文件中,我们提供了一个指导性说明如何利用各种技术,包括新兴的隐性装置、现场可编程门阵列和补充金属氧化物半导体(CMOS)来开发高效的DL加速器,以解决医疗领域各种诊断、模式识别和信号处理问题。此外,我们探索了神经形态处理器和护理点(POC)设备等医疗互联网设备如何补充DL对等处理生物形态信号的医学互联网。我们提供了对神经网络和神经形态硬件应用领域的大量文献的案例研究。我们为各种硬件平台设定了一种感应感应聚合信号处理任务,将电路透半导信号与计算机视野相结合。 在专门的神经形态分析领域和内嵌化机中,在专门的神经形态分析领域和内置的优势方面进行了比较。