Late Breaking Results: Circuit-Algorithm Co-design for Learnable Audio Analog Front-End

Jinhai Hu, Zhongyi Zhang, Cong Sheng Leow, Wang Ling Goh, Yuan Gao

ACM/IEEE Design Automation Conference (DAC) , 2024

This paper presents a circuit-algorithm co-design framework for learnable audio analog front-end (AFE) which includes an analog filterbank for feature extraction and a classifier based on Depthwise Separable Convolutional Neural Network (DSCNN). Instead of the traditional approach to design the analog filterbank and digital classifier separately, a learnable filterbank is proposed and its source-follower bandpass filter (SF-BPF) parameters are optimized together with the neural network classifier in a signal-to-noise ratio (SNR)-aware training process. A new system criterion function (L_BPF) is proposed to include classification loss and filter performance into the training process. The optimized audio AFE achieves 10.6% and 11.7% reduction in BPF power and chip area, respectively. Meanwhile, this approach achieved 88.6%--94.5% accuracy for 10-keyword classification task across a wide range of input signal SNR from 5dB to 20dB, with only 16k trainable parameters.

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