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Machines that do approximate arithmetic offer general purpose computing with the efficiency of specialized hardware (such as deep learning hardware). Software can keep error levels far below what the hardware provides, with little overhead. We demonstrate DARPA-funded prototype hardware, running deep learning and other tasks, and discuss

applications developed in collaboration with MIT, Carnegie Mellon, Sandia, BAE, and others. The technology enables 10,000 core embedded devices and compact, practical billion core systems.

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Joseph Bates has participated in 1 event, Robotics Connect on Sep 28, 2017.
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