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Abstract:

GPU/accelerator architectures have greatly improved the training and inferencing speed for neural-network-based machine learning models. As major industry players race to develop ambitious applications such as self-driving vehicles, unstructured data analytics, human-level interactive systems, and human intelligence augmentation, major challenges remain in computational methods as well as hardware/software infrastructures required for these applications to be effective, robust, responsive, accountable and cost-effective. These applications impose much higher levels of data storage capacity, access latency, energy efficiency, and throughput. In this talk, I will present a vision for building a new generation of computing components and systems for these applications.

Speaker’s 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 (www.crhc.uiuc.edu/Impact). 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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