可穿戴设备的自适应极端边缘计算
Adaptive Extreme Edge Computing for Wearable Devices
AI总结：
本文针对可穿戴设备，提出自适应极端边缘计算的整体视图，涵盖神经形态计算硬件与算法，并评估边缘计算需求及挑战，旨在实现低功耗低延迟的智能感知。
摘要：
可穿戴设备是一种快速增长的技术，对社会和经济中的个人医疗保健都有影响。由于传感器在普及和分布式网络中的广泛部署，功耗、处理速度和系统适应性在未来智能可穿戴设备中至关重要。关于如何将计算推向智能传感器边缘的愿景和预测已经开始，其目标是提供自适应极端边缘计算。在此，我们提供了面向智能可穿戴设备的硬件和理论解决方案的整体视图，可为这一普适计算时代的研究提供指导。我们提出了针对可穿戴传感器的神经形态计算技术中持续学习的生物合理性模型的各种解决方案。为了构想这一概念，我们提供了一个系统性的框架，其中预期了神经形态平台上可穿戴传感器的低功耗和低延迟场景。我们接着描述了利用互补金属氧化物半导体（CMOS）和新兴存储技术（例如忆阻器件）的神经形态处理器的关键潜在领域。此外，我们评估了可穿戴设备内边缘计算在占用空间、功耗、延迟和数据大小方面的需求。我们还调查了神经形态计算硬件、算法和设备之外可能阻碍智能可穿戴设备中自适应边缘计算增强的挑战。
英文摘要：
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
