DeltaKWS: A 65nm 36nJ/Decision Bio-Inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC With 0.6V Near-Threshold SRAM

This paper introduces DeltaKWS, which is to the best of our knowledge, the first $\Delta$RNN-enabled, fine-grained, temporal sparsity-aware Keyword Spotting (KWS) integrated circuit (IC) designed for voice-controlled devices. The 65 nm prototype chip features a number of techniques to enhance performance, area, and power efficiencies, specifically: 1) a bio-inspired delta-gated recurrent neural network ($\Delta$RNN) classifier leveraging temporal similarities between neighboring feature vectors extracted from input frames and network hidden states, eliminating unnecessary operations and memory accesses; 2) an infinite impulse response (IIR) bandpass filter (BPF)-based feature extractor (FEx) that leverages mixed-precision quantization, low-cost computing structure and channel selection; 3) a 24 kB 0.6 V near-$V_{\text{TH}}$ weight static random-access memory (SRAM) that achieves 6.6 $\boldsymbol{\times}$ lower read power than the foundry-provided SRAM. From chip measurement results, we show that the DeltaKWS achieves an 11/12-class Google Speech Command Dataset (GSCD) accuracy of 90.5%/89.5% respectively and energy consumption of 36 nJ/decision in 65 nm CMOS process. At 87% temporal sparsity, computing latency and energy/inference are reduced by 2.4 $\boldsymbol{\times}$/3.4 $\boldsymbol{\times}$, respectively. The IIR BPF-based FEx, $\Delta$RNN accelerator, and 24 kB near-$V_{\text{TH}}$ SRAM blocks occupy 0.084 mm², 0.319 mm², and 0.381 mm² respectively (0.78 mm² in total).