Every Bit Matters: A Hardware/Software Approach for Enabling More Powerful Machine Learning Models

Machine Learning (ML) has empowered computing devices to perform tasks traditionally associated with human intelli-gence, such as “thinking,” “seeing,” “hearing,” “reading,” and “writing.” This capability allows systems to interact with the physical world and process information in ways that can rival human abilities, enhancing discovery, learning, and decision-making. Although the foundational techniques of ML have existed for decades, its recent surge in attention is partly due to advancements in computing hardware. Around 2009, computing hardware in the form of graphics processors had reached a level of performance and storage capacity that made implementing core ML methods practical in terms of cost and time. Despite these advances, the performance and storage capacities of current hardware still constrain ML applications. As ML applications are being deployed across the full spectrum of computing devices, from the data center to Internet-of- Things systems, enhancing hardware remains a critical factor for unlocking ML's full potential.