J. Hu, Z. Zhang, C. S. Leow, W. L. Goh and Y. Gao, "LearnAFE: Circuit-Algorithm Co-Design Framework for Learnable Audio Analog Front-End," in IEEE Transactions on Circuits and Systems I: Regular Papers, doi: 10.1109/TCSI.2025.3578606.
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (ASTAR), Singapore - Nanosystems at the Edge Program
Grant Reference no. : A18A1b0055
This research / project is supported by the Agency for Science, Technology and Research - RIE2025 Manufacturing, Trade and Connectivity (MTC) Programmatic Fund - High Linearity Silicon Germanium Photonic Modulator for 6G Analog Radio over Fiber Project
Grant Reference no. : M24M8b0004