论文标题
始终开在300-NW，事件驱动的尖峰神经网络基于尖峰驱动的时钟产生和时钟和电源，用于超低功率智能设备
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
论文作者
论文摘要
始终在人工智能（AI）功能（例如关键字发现（KWS）和视觉唤醒）等功能往往会主导超低功率设备中的总功耗。一个关键的观察结果是，始终在线功能的信号在时间上很少，因为尖峰神经网络（SNN）分类器可以利用节省功率，因为SNN的开关活动和功耗倾向于随峰值速率扩展。为了实现这一目标，我们为始终开启的功能提供了一种新颖的SNN分类器体系结构，以KWS和其他始终在线分类工作负载的竞争推理精度表明了Sub-300NW功耗。
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.
