Moore's Law and Dennard scaling are waning. Yet the demand for computer systems with ever-increasing computational capabilities and power/energy-efficiency continues unabated, fueled by advances in big data and machine learning. The future of fields as disparate as data analytics, robotics, vision, natural language processing, and more, rests on the continued scaling of system performance per watt, even as traditional CMOS scaling ends.
The following paper proposes a surprising, novel, and creative approach to post-Moore's Law computing by rethinking the digital/analog boundary. The central idea is to revisit the idea of data representation and show how it is a critical design choice that cuts across hardware and software layers.
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