San Francisco Bay ACM
Dr. Wen-mei W. Hwu, Dept. of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Description
Since the rise of deep learning in 2012, much progress has been made in deep-learning-based AI tasks such as image/video understanding and natural language understanding, as well as GPU/accelerator architectures that greatly improve the training and inference speed for neural-network models. As the industry players race to develop ambitious applications such as self-driving vehicles, cashier-less supermarkets, human-level interactive robot systems, and human intelligence augmentation, major research challenges remain in computational methods as well as hardware/software infrastructures required for these applications to be effective, robust, responsive, accountable and cost-effective. Innovations in scale able iterative solvers and graph algorithms will be needed to achieve these application-level goals but will also impose much higher-level of data storage capacity, access latency, energy efficiency, and processing throughput. In this talk, I will present our recent progress in building highly performance AI task libraries, creating full AI applications, providing AI application development tools, and prototyping the Erudite system at the IBM-Illinois C3SR.
Speaker Bio
Wen-mei W. Hwu is a Professor and holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. He is the director of the IMPACT research group (http://impact.crhc.illinois.edu). He co-directs the IBM-Illinois Center for Cognitive Computing Systems Research (C3SR) and serves as one of the principal investigators of the NSF Blue Waters Petascale supercomputer. For his contributions, he received the ACM SigArch Maurice Wilkes Award, the ACM Grace Murray Hopper Award, the IEEE Computer Society Charles Babbage Award, the ISCA Influential Paper Award, the IEEE Computer Society B. R. Rau Award and the Distinguished Alumni Award in Computer Science of the University of California, Berkeley. He is a fellow of IEEE and ACM. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley
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