让音频模拟前端与分类器联合优化，提升低功耗设备的识别精度。
LearnAFE: Circuit-Algorithm Co-design Framework for Learnable Audio Analog Front-End
- 将滤波器传输函数与分类器一起训练，实现系统级最优
- 在5-20 dB SNR下准确率达90.5%-94.2%，仅用22k参数
- 适合低功耗嵌入式语音识别，如智能耳机、可穿戴设备
本文提出一种面向音频信号分类的可学习模拟前端（AFE）电路-算法协同设计框架。传统上，AFE与后端分类器分别设计，但本文证明这种做法非最优。为此，本文提出将后端分类器与AFE的传输函数进行联合优化，以实现系统级最优。具体而言，在信噪比（SNR）感知的训练循环中，对模拟带通滤波器（BPF）阵列的传输函数参数进行调优。通过引入协同设计损失函数LBPF，实现了滤波器组与分类器的共同优化。该设计基于开源SKY130 130nm CMOS工艺实现，在5-20 dB输入信号信噪比范围内，10关键词分类任务准确率达到90.5%-94.2%，且分类器仅需22,000个参数。相比传统方法，所提音频AFE在功耗和电容面积上分别降低8.7%和12.9%。
原文摘要 · Abstract (English)
This paper presents a circuit-algorithm co-design framework for learnable analog front-end (AFE) in audio signal classification. Designing AFE and backend classifiers separately is a common practice but non-ideal, as shown in this paper. Instead, this paper proposes a joint optimization of the backend classifier with the AFE's transfer function to achieve system-level optimum. More specifically, the transfer function parameters of an analog bandpass filter (BPF) bank are tuned in a signal-to-noise ratio (SNR)-aware training loop for the classifier. Using a co-design loss function LBPF, this work shows superior optimization of both the filter bank and the classifier. Implemented in open-source SKY130 130nm CMOS process, the optimized design achieved 90.5%-94.2% accuracy for 10-keyword classification task across a wide range of input signal SNR from 5 dB to 20 dB, with only 22k classifier parameters. Compared to conventional approach, the proposed audio AFE achieves 8.7% and 12.9% reduction in power and capacitor area respectively.
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