PySyn: A Rapid Synthesis for Mixed-Signal Machine Learning Classification

Abstract

Mixed-signal integrated circuits (ICs) for machine learning (ML) have been demonstrated as a powerful tool for efficient and accurate classification of large volumes of complex data. Despite the growing interest in ML ICs, the design process of mixed-signal ML classifiers is dominated by ad hoc approaches. In this paper, a rapid synthesizer is developed in Python (PySyn) for designing compact power-efficient high-performance ML classifiers. Circuit-level ML library is designed and leveraged within the flow. System-level tradeoffs are generated with PySyn and utilized to iteratively adjust the ML performance. PySyn is demonstrated with a state-of-the-art classifier, generating optimized netlists under input constraints.