⚡ 本页包含 AI 生成的分析内容，仅供参考
本文提出了一种94.8nW的无电池智能硅平台，通过分布式、自适应和事件驱动的多模态传感技术，在边缘实现机器学习分析，解决了超低功耗设备在低至100nW环境能量下的持续运行问题。
Haochen Zhang1, Wei-Han Yu1, Zhongyu Zhao1, Zhizhan Yang1, Ka-Fai Un1, Jun Yin1, Rui P. Martins1,2, Pui-In Mak1 University of Macau, Macau, China Instituto Superior Tecnico/University of Lisboa, Lisbon, Portugal 1 2 Sensor nodes with machine learning (ML) are adept at analyzing intricate environmental and physiological data patterns at the edge [1-9]. The design of such ultra-low-power (ULP) devices strives to reduce power consumption, which ensures continuous and energy-harvested operation even with fluctuating ambient available energy levels down to 100nW [10-11]. Consequently, ML capabilities on such ULP devices are constrained to perform lightweight detection for events such as voice activity [3][6], arrhythmia [4][8], and bearing anomalies [9]. Yet, these isolated, monomodal sensing paradigms suffer from low task complexity and accuracy for overlooking fused information from spatially
Distributed, Adaptive, and Event-Driven Multimodal Sensing at, the Edge