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Fishbowl Teleseminar: Approximate Acceleration: Trading off Certainty for Efficiency and Speed

November 13, 2014 @ 2:30 am - 4:00 pm

Hadi Esmaeilzadeh
Assistant Professor
Dept. of Computer Science
Georgia Institute of Technology


As our Dark Silicon study shows, the benefits from continuous transistor scaling are diminishing due to energy and power constraints. Further, our results show that the current paradigm of general-purpose processors, multicore processors, will fall significantly short of historical trends of performance improvements in the next decade. These shortcomings may drastically curtail computing industry from continuously delivering new capabilities, the backbone of its economic ecosystem. To this end, radical departures from conventional approaches are necessary to provide continued performance and efficiency gains in computing. In this talk, I will present our work on general-purpose approximate computing across the system stack as one possible path forward. Specifically, I will talk about our hybrid analog-digital general-purpose processor that executes programs written in conventional languages. Our hybrid processor, leverage an approximate algorithmic transformation that converts regions of code from a Von Neumann model to a neuromorphic model bridging the two models of computing. I will also briefly discuss how we leverage the approximate nature of programs to tackle memory subsystem bottlenecks. Our work shows significant gains in general-purpose computing when the abstraction of near-perfect accuracy is relaxed and opens new venues for research and development.


Hadi Esmaeilzadeh is the Catherine M. and James E. Allchin Early Career Professor of Computer Science at Georgia Institute of Technology. His Ph.D. dissertation received the 2013 William Chan Memorial Dissertation Award from University of Washington. He founded the Alternative Computing Technologies (ACT) Lab, where he works with his students on developing new technologies and cross-stack solutions to build the next generation computing systems for emerging applications. Hadi received his Ph.D. in Computer Science and Engineering from University of Washington in 2013. He has a Master’s degree in Computer Science from The University of Texas at Austin (2010), and a Master’s degree in Electrical and Computer Engineering from University of Tehran (2005). Hadi received the Google Research Faculty Award in 2013 and his team was awarded the Qualcomm Innovation Fellowship in 2014. Hadi’s research is recognized by three Communications of the ACM Research Highlights and three IEEE Micro Top Picks. His work on dark silicon has been profiled in New York Times.



November 13, 2014
2:30 am - 4:00 pm