A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 06V Near-Threshold SRAM
<|reference_start|>A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 06V Near-Threshold SRAM: 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.<|reference_end|>
@article{chen2024a,
    title={A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital
  Keyword Spotting IC with 0.6V Near-Threshold SRAM},
    author={Qinyu Chen, Kwantae Kim, Chang Gao, Sheng Zhou, Taekwang Jang, Tobi
  Delbruck, Shih-Chii Liu},
    journal={arXiv preprint arXiv:2405.03905},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03905},
    primaryClass={cs.AR cs.CV cs.SD eess.AS}
}