  This paper introduces, to the best of the authors' knowledge, the first
fine-grained temporal sparsity-aware keyword spotting (KWS) IC leveraging
temporal similarities between neighboring feature vectors extracted from input
frames and network hidden states, eliminating unnecessary operations and memory
accesses. This KWS IC, featuring a bio-inspired delta-gated recurrent neural
network ({\Delta}RNN) classifier, achieves an 11-class Google Speech Command
Dataset (GSCD) KWS accuracy of 90.5% and energy consumption of 36nJ/decision.
At 87% temporal sparsity, computing latency and energy per inference are
reduced by 2.4$\times$/3.4$\times$, respectively. The 65nm design occupies
0.78mm$^2$ and features two additional blocks, a compact 0.084mm$^2$ digital
infinite-impulse-response (IIR)-based band-pass filter (BPF) audio feature
extractor (FEx) and a 24kB 0.6V near-Vth weight SRAM with 6.6$\times$ lower
read power compared to the standard SRAM.
