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Columbia University’s Data Science Institute Presents:

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Wednesday, April 6 • 2:00pm - 4:30pm
A 450mV Timing-Margin-Free Unsupervised Sorter based on Spiking Neural Network [P11]

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In the on-going quest to enabling energy-efficient cognitive computing, specialized, often non-Von-Neumann, architecture in near/sub-VTH circuits emerges as a promising candidate. However, the complex and parallel nature of the architecture combined with the large delay variability from near/sub-VTH circuits impose prohibitive timing margin to cycle time and limit the achievable efficiency. The conventional error detection and correction (EDAC) techniques can remove margin, however, they can cause significant overhead since most of them target super-VTH in-order microprocessors. In this work, EDAC technique suitable for near/sub-VTH complex and parallel architecture is demonstrated in an unsupervised sorter based on spiking neural network for brain-computer-interface microsystems using 1) body boost correction, 2) fully-static error-detecting latch, 3) sparse error detection, and 4) local-buffer-less latches and clock distribution. The technique improves energy-efficiency by 49.3% and FCLK by 35.6% compared to the baseline with margin at the area overhead of 4.1%. No additional VDD is needed.

Demo/Poster Presenter
avatar for Josh Seongjong Kim

Josh Seongjong Kim

PhD Candidate in Electrical Engineering, Columbia Engineering
Josh Seongjong Kim received his B.S. degree in electrical engineering from Hanyang University, South Korea, in 2010, and his M.S. degree from University of Michigan in 2012. He is currently pursuing Ph.D. degree in electrical engineering at Columbia University, New York. His research interests include low-power and variation tolerant VLSI circuit and system design. He is a student fellow of the Catalyst Foundation  | (e) sk3667 at... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040