A 174nW Event-Driven Keyword Spotting ASIC with DC-Drift-Resilient Dual-Mode Delta/SAR Quantizer and Multiplier-Less Processor

Recently, significant progress has been achieved in low-power keyword spotting (KWS) ASICs, which can serve as always-on wake-up function blocks for computationally intensive speech processing tasks. A 3-class CNN based KWS classifier consumed 510 nW power without including on-chip ADCs [1]. In [2], an ultra-low power event-driven level-crossing (LC) ADC was employed, reducing standby power to 148 nW. However, LC-ADC is vulnerable to DC drift. Other works include a 12-class KWS chip consuming $23 \mu \mathrm{~W}$ power [3], and a skip RNN-based 7-class KWS chip consuming 1.5 $\mu \mathrm{W}$ power [4]. Despite these technological advancements, researchers are still seeking KWS solutions with even lower power consumption, while not compromising on other key specifications such as keyword variety, classification accuracy and latency.

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Published
2024-11-18
DOI
https://doi.org/10.1109/a-sscc60305.2024.10849242
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

A 174nW Event-Driven Keyword Spotting ASIC with DC-Drift-Resilient Dual-Mode Delta/SAR Quantizer and Multiplier-Less Processor

Kaiji Liu, Nan Wu, Ning Pu, Sining Pan et al.
1 links
Advanced Memory and Neural Computing
article

A 174nW Event-Driven Keyword Spotting ASIC with DC-Drift-Resilient Dual-Mode Delta/SAR Quantizer and Multiplier-Less Processor

Kaiji Liu, Nan Wu, Ning Pu, Sining Pan, Hanjun Jiang, Zhihua Wang, Yanshu Guo, Siqi Zhang, Yihuai Yang
article en

Abstract

Recently, significant progress has been achieved in low-power keyword spotting (KWS) ASICs, which can serve as always-on wake-up function blocks for computationally intensive speech processing tasks. A 3-class CNN based KWS classifier consumed 510 nW power without including on-chip ADCs [1]. In [2], an ultra-low power event-driven level-crossing (LC) ADC was employed, reducing standby power to 148 nW. However, LC-ADC is vulnerable to DC drift. Other works include a 12-class KWS chip consuming $23 \mu \mathrm{~W}$ power [3], and a skip RNN-based 7-class KWS chip consuming 1.5 $\mu \mathrm{W}$ power [4]. Despite these technological advancements, researchers are still seeking KWS solutions with even lower power consumption, while not compromising on other key specifications such as keyword variety, classification accuracy and latency.

Tsinghua University (CN)
National Science and Technology Major Project
Openalex Percentile: Top 76%
Advanced Memory and Neural Computing
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A 174nW Event-Driven Keyword Spotting ASIC with DC-Drift-Resilient Dual-Mode Delta/SAR Quantizer and Multiplier-Less Processor — Kaiji Liu, Nan Wu, et al. · (2024) | TGRS Research Map | TGRS