Dow Distinguished Lecture Series|
Efficient Hardware and Methods for Deep Learning
William J. Dally
Chief Scientist and Senior Vice President of Research
Wednesday, March 14, 2018|
3:00pm - 4:00pm
Johnson Rooms, Lurie Engineering Center, 3rd floor
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|The William Gould Dow Distinguished Lectureship is the highest external honor bestowed by the Department, and recognizes the accomplishments of external individuals who have made outstanding contributions in the field of Electrical Engineering and Computer Science.|
About the Event
The current resurgence of artificial intelligence is due to advances in deep learning. Systems based on deep learning now exceed human capability in speech recognition, object classification, and playing games like Go. Deep learning has been enabled by powerful, efficient computing hardware. The algorithms used have been around since the 1980s, but it has only been in the last few years - when powerful GPUs became available to train networks - that the technology has become practical. This talk will review the current state of deep learning and describe recent research on making these systems more efficient.
Bill is Chief Scientist and Senior Vice President of Research at NVIDIA Corporation and a Professor (Research) and former chair of Computer Science at Stanford University. Bill is currently working on developing hardware and software to accelerate demanding applications including machine learning, bioinformatics, and logical inference. He has a history of designing innovative and efficient experimental computing systems. While at Bell Labs Bill contributed to the BELLMAC32 microprocessor and designed the MARS hardware accelerator. At Caltech he designed the MOSSIM Simulation Engine and the Torus Routing Chip which pioneered wormhole routing and virtual-channel flow control. At the Massachusetts Institute of Technology his group built the J-Machine and the M-Machine, experimental parallel computer systems that pioneered the separation of mechanisms from programming models and demonstrated very low overhead synchronization and communication mechanisms. At Stanford University his group has developed the Imagine processor, which introduced the concepts of stream processing and partitioned register organizations, the Merrimac supercomputer, which led to GPU computing, and the ELM low-power processor.
Sponsor(s): Electrical Engineering and Computer Science
Faculty Sponsor: Dennis Sylvester
Open to: Public