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.
Papers
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Journal ArticleDOI
FPGA programming for the masses
TL;DR: The programmability of FPGAs must improve if they are to be part of mainstream computing, and this paper presents a meta-modelling architecture suitable for this purpose.
Proceedings ArticleDOI
A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference
Bruce M. Fleischer,Sunil Shukla,Matthew M. Ziegler,Joel Abraham Silberman,Jinwook Oh,Vijavalakshmi Srinivasan,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,Lel Chang,Kailash Gopalakrishnan +30 more
TL;DR: A multi-TOPS AI core is presented for acceleration of deep learning training and inference in systems from edge devices to data centers by employing a dataflow architecture and an on-chip scratchpad hierarchy.
Proceedings ArticleDOI
Approximate computing: Challenges and opportunities
Ankur Agrawal,Jungwook Choi,Kailash Gopalakrishnan,Suyog Gupta,Ravi Nair,Jinwook Oh,Daniel A. Prener,Sunil Shukla,Vijayalakshmi Srinivasan,Zehra Sura +9 more
TL;DR: It is shown that hot loops in the applications can be perforated by an average of 50% with proportional reduction in execution time, while still producing acceptable quality of results, and that benefits compounded when these techniques are applied concurrently.
Journal ArticleDOI
FPGA Programming for the Masses: The programmability of FPGAs must improve if they are to be part of mainstream computing.
TL;DR: When looking at how hardware influences computing performance, the authors have GPPs (general-purpose processors) on one end of the spectrum and ASICs (application-specific integrated circuits) on the other.
Proceedings ArticleDOI
A compiler and runtime for heterogeneous computing
Joshua S. Auerbach,David F. Bacon,Ioana Monica Burcea,Perry Cheng,Stephen J. Fink,Rodric Rabbah,Sunil Shukla +6 more
TL;DR: Liquid Metal is presented, a comprehensive compiler and runtime system for a new programming language called Lime that enables the use of a single language for programming heterogeneous computing platforms, and the seamless co-execution of the resultant programs on CPUs and accelerators that include GPUs and FPGAs.