Energy-Efficient Machine Learning Acceleration: From Technologies to Circuits and Systems

Advanced computing systems have long been enablers for breakthroughs in Machine Learning (ML) algorithms either through sheer computational power or form-factor miniaturization. However, as ML algorithms become more complex and the size of datasets increase, existing computing platforms are no longer sufficient to bridge the gap between algorithmic innovation and hardware design. With the rising needs of advanced algorithms for large-scale data analysis and data-driven discovery, and significant growth in emerging applications from the edge to the cloud, we need energy-efficient, low-cost, high- performance, and reliable computing systems targeted for these applications. This paper presents the latest developments in oscillatory neural networks, optical computing, and memristive processing-in-memory (PIM) to address the various challenges in designing efficient computing systems specifically targeting ML applications.