DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM
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| Format: | Preprint |
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2024
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| author | Chen, Qinyu Kim, Kwantae Gao, Chang Zhou, Sheng Jang, Taekwang Delbruck, Tobi Liu, Shih-Chii |
| author_facet | Chen, Qinyu Kim, Kwantae Gao, Chang Zhou, Sheng Jang, Taekwang Delbruck, Tobi Liu, Shih-Chii |
| contents | This paper introduces DeltaKWS, to the best of our knowledge, the first $Δ$RNN-enabled fine-grained temporal sparsity-aware KWS IC 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 ($Δ$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 IIR BPF-based 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 SRAM that achieves 6.6X lower read power than the foundry-provided SRAM. From chip measurement results, we show that the DeltaKWS achieves an 11/12-class 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 2.4X/3.4X, respectively. The IIR BPF-based FEx, $Δ$RNN accelerator, and 24 kB near-$V_\text{TH}$ SRAM blocks occupy 0.084 mm$^{2}$, 0.319 mm$^{2}$, and 0.381 mm$^{2}$ respectively (0.78 mm$^{2}$ in total). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03905 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM Chen, Qinyu Kim, Kwantae Gao, Chang Zhou, Sheng Jang, Taekwang Delbruck, Tobi Liu, Shih-Chii Hardware Architecture Computer Vision and Pattern Recognition Sound Audio and Speech Processing This paper introduces DeltaKWS, to the best of our knowledge, the first $Δ$RNN-enabled fine-grained temporal sparsity-aware KWS IC 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 ($Δ$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 IIR BPF-based 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 SRAM that achieves 6.6X lower read power than the foundry-provided SRAM. From chip measurement results, we show that the DeltaKWS achieves an 11/12-class 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 2.4X/3.4X, respectively. The IIR BPF-based FEx, $Δ$RNN accelerator, and 24 kB near-$V_\text{TH}$ SRAM blocks occupy 0.084 mm$^{2}$, 0.319 mm$^{2}$, and 0.381 mm$^{2}$ respectively (0.78 mm$^{2}$ in total). |
| title | DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM |
| topic | Hardware Architecture Computer Vision and Pattern Recognition Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2405.03905 |