Hardware Acceleration For Machine Learning Professors in Global

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Jiaqi Gu
Arizona State University China
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gpu acceleration
emerging hardware design for efficient computing
efficient ai hardware design
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Jiang Hu
Texas A&M University United States of America
artificial intelligence hardware optimization
machine learning
hardware security
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Anatole Von Lilienfeld
University of Toronto Canada
high-performance computing
machine learning
quantum machine learning
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George Amvrosiadis
Carnegie Mellon University United States of America
high performance computing
machine learning
scalable machine learning
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Tze Low
Carnegie Mellon University United States of America
hardware
machine learning
parallel high-performance computing
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Eric Darve
Stanford University United States of America
high-performance computing
machine learning
gpu computing
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Mohamed Abdelfattah
Cornell University Netherlands
hardware architectures
machine learning
automated machine learning
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Zhiru Zhang
Cornell University Netherlands
accelerator architectures
machine learning
algorithm-hardware co-design
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Bernd Dammann
Technical University of Denmark Denmark
high-performance computing
gpu computing
large scale computations
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Ian Jeffrey
University of Manitoba Canada
high-performance computing
gpu computing
applied machine learning
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Danica Sutherland
University of British Columbia Canada
machine learning
statistical machine learning
theoretical machine learning
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Simon Mcintosh-smith
University of Bristol United Kingdom
high performance computing
gpu computing
multi-core processors
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Richard D'arcy
University of Oxford United Kingdom
novel accelerators
plasma accelerators
conventional accelerators
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Carl Schroeder
University of California, Berkeley United States of America
plasma-based accelerators
advanced acceleration concepts
accelerator science & technology
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Beidi Chen
Carnegie Mellon University United States of America
hardware acceleration for machine learning
efficient machine learning
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David Pan
University of Texas at Austin United States of America
hardware acceleration
efficient machine learning
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David Pan
University of Texas at Austin United States of America
hardware acceleration
machine learning
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Young Cho
University of Southern California United States of America
hardware acceleration
machine learning
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Gary Grewal
University of Guelph Canada
hardware acceleration
machine learning
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David Pan
University of Texas at Austin United States of America
hardware acceleration
machine learning
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Lei Zhang
University of Regina Canada
hardware acceleration
machine learning
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