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Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training



Charbel Sakr , Steve Dai , Rangharajan Venkatesan , Brian Zimmer , William J. Dally , Brucek Khailany

* Published as a spotlight paper at ICML 2022. Paper contains 16 pages, 5 figures, and 6 tables 

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Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications



Sujan Kumar Gonugondla , Charbel Sakr , Hassan Dbouk , Naresh R. Shanbhag

* 14 pages, 13 figures 

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HarDNN: Feature Map Vulnerability Evaluation in CNNs



Abdulrahman Mahmoud , Siva Kumar Sastry Hari , Christopher W. Fletcher , Sarita V. Adve , Charbel Sakr , Naresh Shanbhag , Pavlo Molchanov , Michael B. Sullivan , Timothy Tsai , Stephen W. Keckler

* 14 pages, 5 figures, a short version accepted for publication in First Workshop on Secure and Resilient Autonomy (SARA) co-located with MLSys2020 

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Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks



Charbel Sakr , Naigang Wang , Chia-Yu Chen , Jungwook Choi , Ankur Agrawal , Naresh Shanbhag , Kailash Gopalakrishnan

* Published as a conference paper in ICLR 2019 

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Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm



Charbel Sakr , Naresh Shanbhag

* Published as a conference paper in ICLR 2019 

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Understanding the Energy and Precision Requirements for Online Learning



Charbel Sakr , Ameya Patil , Sai Zhang , Yongjune Kim , Naresh Shanbhag

* 14 pages, 5 figures 4 of which have 2 subfigures 

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