A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM

arXiv:2405.03905v1 Announce Type: cross
Abstract: 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 …

abstract arxiv authors best of bio bio-inspired cs.ar cs.cv cs.sd decision digital eess.as feature fine-grained hidden knowledge memory near network operations paper sparsity temporal threshold type vectors