论文标题
可穿戴设备的自适应极端边缘计算
Adaptive Extreme Edge Computing for Wearable Devices
论文作者
论文摘要
可穿戴设备是一种快速增长的技术,对社会和经济的个人医疗保健产生影响。由于传统和分布式网络中传感器的广泛扩展,因此功耗,处理速度和系统适应性对于将来的智能可穿戴设备至关重要。智能传感器中如何将计算提高到边缘的视觉和预测已经开始,渴望提供自适应的极端边缘计算。在这里,我们为智能可穿戴设备提供了硬件和理论解决方案的整体视图,可以为这个普遍的计算时代提供指导。我们为在可穿戴传感器的神经形态计算技术中持续学习的生物学上合理模型提出了各种解决方案。为了设想这个概念,我们提供了一个系统的概述,其中预期在神经形态平台中可穿戴传感器的潜在低功率和低潜伏期情景。我们依次描述了利用互补金属氧化物半导体(CMOS)和新兴记忆技术(例如,熟悉的设备)的神经形态处理器的重要潜在景观。此外,我们根据足迹,功耗,延迟和数据大小评估可穿戴设备内边缘计算的要求。我们还研究了神经形态计算硬件,算法和设备以外的挑战,这些算法和设备可能会阻碍智能可穿戴设备中自适应边缘计算的增强。
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.