Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device

Autor:	Wang, Dewei, Chundi, Pavan Kumar, Kim, Sung Justin, Yang, Minhao, Cerqueira, Joao Pedro, Kang, Joonsung, Jung, Seungchul, Kim, Sangjoon, Seok, Mingoo
Rok vydání:	2020
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Always-on artificial intelligent (AI) functions such as keyword spotting (KWS) and visual wake-up tend to dominate total power consumption in ultra-low power devices. A key observation is that the signals to an always-on function are sparse in time, which a spiking neural network (SNN) classifier can leverage for power savings, because the switching activity and power consumption of SNNs tend to scale with spike rate. Toward this goal, we present a novel SNN classifier architecture for always-on functions, demonstrating sub-300nW power consumption at the competitive inference accuracy for a KWS and other always-on classification workloads.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.12314 Zobrazit plný text záznamu View this record from Arxiv