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Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA
Joel Hestness

Joel Hestness

Joel Hestness is a systems research scientist at Baidu Research Silicon Valley AI Lab (SVAIL). He studies the scaling characteristics of machine and deep learning applications and techniques to scale out model training runs on large-scale clusters. His prior research focused on general-purpose GPU microarchitecture and memory hierarchies to improve programmability, performance, and energy efficiency in heterogeneous processors. Joel contributes to gem5-gpu, gem5, and TensorFlow. He holds a PhD in computer architecture from the University of Wisconsin-Madison.


1:45pm-2:25pm Friday, September 7, 2018
Location: Yosemite BC
Secondary topics:  Platforms and infrastructure, Text, Language, and Speech
Joel Hestness (Baidu)
Deep learning (DL) creates impactful advances following a virtuous recipe: a model architecture search, creating large training datasets, and scaling computation. Joel Hestness discusses research done by Baidu Research's Silicon Valley AI Lab on new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing. Read more.