Efficient Stochastic Machine Learning at the Edge
In this talk, I will talk about some hardware/software work my group has done in the area of stochastic computing-based machine learning acceleration. Stochastic computing or SC is an approximate, stream-based computing paradigm enabling extremely area-efficient implementations of basic arithmetic operations such as multiplication and addition. I will talk about the suitability of the SC to the machine learning/event processing workloads, how to deal with its inherent approximate nature and briefly discuss few chip prototypes that leverage both logic and in-memory implementations of SC-based accelerators for dense as well as a sparse compute.
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- Starts 29 May 2026 07:00 AM UTC
- Ends 25 June 2026 01:55 AM UTC
- No Admission Charge
Speakers
Puneet Gupta of UCLA
Biography:
Puneet Gupta received the B.Tech. degree in electrical engineering from the Indian Institute of Technology Delhi, New Delhi, India, in 2000, and the Ph.D. degree from the University of California at San Diego, San Diego, CA, USA, in 2007. He is currently a Faculty Member with the Electrical and Computer Engineering Department, University of California at Los Angeles. He Co-Founded Blaze DFM Inc., Sunnyvale, CA, USA, in 2004 and served as its Product Architect until 2007. He has authored over 200 papers, 18 U.S. patents, a book and two book chapters in the areas of design-technology co-optimization as well as variability/reliability aware architectures. Dr. Gupta is an IEEE Fellow and was a recipient of the NSF CAREER Award, the ACM/SIGDA Outstanding New Faculty Award, SRC Inventor Recognition Award, and the IBM Faculty Award. He has led the multi-university IMPACT+ Center which focused on future semiconductor technologies. He currently leads the System Benchmarking theme within the SRC CHIMES JUMP 2.0 center.
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Address:Los Angeles