S
Sunil Shukla
Researcher at IBM
Publications - 43
Citations - 913
Sunil Shukla is an academic researcher from IBM. The author has contributed to research in topics: Field-programmable gate array & Reconfigurable computing. The author has an hindex of 12, co-authored 41 publications receiving 671 citations. Previous affiliations of Sunil Shukla include University of Queensland & Karlsruhe Institute of Technology.
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Patent
Programmable data delivery to a system of shared processing elements with shared memory
Chia-Yu Chen,Jungwook Choi,Brian W. Curran,Bruce M. Fleischer,Gopalakrishan Kailash,Jinwook Oh,Sunil Shukla,Vijayalakshmi Srinivasan,Swagath Venkataramani +8 more
TL;DR: In this article, the authors present an embodiment for communicating memory between a plurality of computing components, including load agents and store agents on the processing chip, each interfacing with the plurality of memory components.
Patent
Programmable data delivery by load and store agents on a processing chip interfacing with on-chip memory components and directing data to external memory components
Chia-Yu Chen,Jungwook Choi,Brian W. Curran,Bruce M. Fleischer,Gopalakrishan Kailash,Jinwook Oh,Sunil Shukla,Vijayalakshmi Srinivasan,Swagath Venkataramani +8 more
TL;DR: In this article, the authors present an embodiment for communicating memory between a plurality of computing components, including load agents and store agents on the processing chip, each interfacing with the plurality of memory components.
Proceedings ArticleDOI
Across the Stack Opportunities for Deep Learning Acceleration
Vijayalakshmi Srinivasan,Bruce M. Fleischer,Sunil Shukla,Matthew M. Ziegler,Joel Abraham Silberman,Jinwook Oh,Jungwook Choi,Silvia Melitta Mueller,Ankur Agrawal,Tina Babinsky,Nianzheng Cao,Chia-Yu Chen,Pierce Chuang,Thomas W. Fox,George D. Gristede,Michael A. Guillorn,Howard M. Haynie,Michael J. Klaiber,Dongsoo Lee,Shih-Hsien Lo,Gary W. Maier,Michael R. Scheuermann,Swagath Venkataramani,Christos Vezyrtzis,Naigang Wang,Fanchieh Yee,Ching Zhou,Pong-Fei Lu,Brian W. Curran,Leland Chang,Kailash Gopalakrishnan +30 more
TL;DR: A multi-TOPS AI core for acceleration of deep learning training and inference in systems from edge devices to data centers is presented and it is demonstrated that to derive high sustained utilization and energy efficiency from the AI core requires ground-up re-thinking to exploit approximate computing across the stack including algorithms, architecture, programmability, and hardware.