Background Noise and Process-Variation-Tolerant Sub-Microwatt Keyword Spotting Hardware Featuring Spike-Domain Division-Based Energy Normalization

Abstract

This article presents keyword spotting (KWS) hardware that uses analog signal processing (ASP) and division-based energy normalization (DN) to perform ultralow power KWS robustly across unknown background noise conditions. Most existing KWS systems employed noise-dependent training, limiting their accuracy under different noise conditions. Some other works used noise-independent training but required a larger classifier to learn more features under different noise cases. In this work, we prototyped the KWS hardware using two chips: a normalized acoustic feature extractor (NAFE) chip in a 65-nm CMOS and a spiking neural network (SNN) classifier chip in the same technology. The proposed hardware can perform an end-to-end KWS task while achieving robustness across various signal-to-noise ratios (SNRs) and different noise types. In addition, it can mitigate the effects of the process and temperature variations of the ASP circuits, further improving accuracy. The proposed hardware is also fully event-driven, minimizing power consumption when the input activity is low. The end-to-end KWS system consumes 220 to 570 nW across 0%–100% input activities and achieves 96.5%–88.0% accuracy across 20 to −5 dB SNRs on the HeySnips dataset.