부산대학교 도서관

Recent advances in margin-propagation (MP) based approximate computing have resulted in analog computing circuits that exhibit scaling properties similar to that of digital computing circuits. MP-based circuits allow trading off energy-efficiency with speed and precision, endow robustness to temperature variations, and make the design portable across different process nodes. In this work, We leverage these scaling properties to design ARYABHAT, a field-programmable analog machine learning processor that can be synthesized like digital field-programmable gate arrays (FPGAs). ARYABHAT features a fully reconfigurable tile-based modular analog architecture with adjustable throughput and configurable energy requirements, making it suitable for various machine-learning computations. The architecture can perform computations at variable accuracy and different power-performance specifications and can simultaneously leverage near-memory computing paradigms to improve computational throughput. We also present a complete programming and test ecosystem for ARYABHAT called ARYAFlow and ARYATest. As proof of concept, we showcase the implementation of machine learning algorithms at different performance specifications.