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ML-EXray: Visibility into ML deployment on the edge

Published in MLSys, 2022

ML-EXray provides visibility into layer-level details of the ML execution, helps developers analyze and debug cloud-to-edge deployment issues.

Recommended citation: Qiu, Hang, Ioanna Vavelidou, Jian Li, Evgenya Pergament, Pete Warden, Sandeep Chinchali, Zain Asgar, and Sachin Katti. "ML-EXray: Visibility into ML deployment on the edge." Proceedings of Machine Learning and Systems 4 (2022): 337-351.
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G-QED: Generalized QED pre-silicon verification beyond non-interfering hardware accelerators

Published in DAC, 2023

G-QED (Generalized QED) goes beyond non-interfering HAs, providing a quick and provably thorough HA verification framework that does not require extensive design-specific properties or a full functional specification.

Recommended citation: S. Chattopadhyay et al., "G-QED: Generalized QED Pre-silicon Verification beyond Non-Interfering Hardware Accelerators," 2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2023, pp. 1-6, doi: 10.1109/DAC56929.2023.10247903. keywords: {Productivity;Context;Design automation;Digital systems;Side-channel attacks;Energy efficiency;Timing;QED;Quick Error Detection;Accelerators;Processors;Functional consistency},
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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