# A 23 μW Keyword Spotting IC with Ring-Oscillator-Based Time-Domain Feature Extraction

arXiv: https://arxiv.org/abs/2208.00693 · HTML: https://mlsanity.com/paper/2208.00693/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data](https://mlsanity.com/paper/2505.24852/) (2025)
- [DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM](https://mlsanity.com/paper/2405.03905/) (2024)
- [LearnAFE: Circuit-Algorithm Co-design Framework for Learnable Audio Analog Front-End](https://mlsanity.com/paper/2507.00755/) (2025)
- [Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning](https://mlsanity.com/paper/2607.29353/) (2026)

## Abstract (arXiv)

This article presents the first keyword spotting (KWS) IC which uses a ring-oscillator-based time-domain processing technique for its analog feature extractor (FEx). Its extensive usage of time-encoding schemes allows the analog audio signal to be processed in a fully time-domain manner except for the voltage-to-time conversion stage of the analog front-end. Benefiting from fundamental building blocks based on digital logic gates, it offers a better technology scalability compared to conventional voltage-domain designs. Fabricated in a 65 nm CMOS process, the prototyped KWS IC occupies 2.03mm2 and dissipates 23 μW power consumption including analog FEx and digital neural network classifier. The 16-channel time-domain FEx achieves 54.89 dB dynamic range for 16 ms frame shift size while consuming 9.3 μW. The measurement result verifies that the proposed IC performs a 12-class KWS task on the Google Speech Command Dataset (GSCD) with >86% accuracy and 12.4 ms latency.