A 174/756-nW 6-Class Keyword Spotting ASIC With Delta/Successive-Approximation Dual-Mode Quantizer

An ultra-low-power (ULP) six-class keyword spotting (KWS) ASIC is presented in this article, which can be used in always-on speech-based human–machine interface applications. The ASIC is composed of a sampling frequency and resolution adaptive (SFRA) dual-mode analog-to-digital quantizer and a power-gated KWS engine with a multiplier-less processing element (PE) array. The dual-mode quantizer mainly operates as a 1.5-bit delta quantizer (DQ), providing inherent robustness against dc drift. It adaptively switches to high-resolution successive-approximation register (SAR) quantization mode upon detecting a sound event based on the DQ output. In the KWS engine, the multiplier-less PE array is reused for both feature extraction and gated recurrent unit (GRU)-based keyword classification. To unveil the tradeoffs between power consumption and flexibility, two versions of the classifier have been implemented, with ROM-based on-chip weight memory (WM) and SRAM-based WM. Fabricated in 180-nm CMOS technology, the proposed KWS ASIC with ROM and SRAM-based WM achieves six-class classification accuracies of 87.3% and 90.1%, respectively, on the Google Speech Command dataset (GSCD) while consuming 174 and 756 nW long-term average (LTA) power with a decision latency of 14 ms at a clock frequency of 256 kHz.