Embedded Systems

Dynamic Range and Complexity Optimization of Mixed-Signal Machine Learning Systems

by Naci Pekcokguler, Dominique Morche, Adrian Frischknecht, Christoph Gerum, Andreas Burg, and Catherine Dehollain
In 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1-5, 2021.

Keywords: keyword spotting, analog feature extraction, machine learning classifier, dynamic range reduction, system complexity optimization


Audio processing had been in demand throughout the electronic era. Recent advances in neural networks increased the demand on audio processing for speech recognition applications. In this work, a rigorous study on the dynamic range and system complexity optimization is presented for a mixed-signal keyword spotting system. The proposed system consists of an analog feature extractor and a neural network based keyword classifier. The results showed that with the proposed method, more than an order of magnitude power saving can be achieved in the analog feature extraction compared to the digital state-of-the-art counterpart.