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Meeting ID: 505 935 226
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Meeting ID: 505 935 226
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Talk Title: An Integrated Approach for Efficient Neural Network Design, Training, and Inference
Talk Abstract: One of the main challenges in designing, training, and implementing Neural Networks is their high demand for computational and memory resources. Designing a model for a new task requires searching through an exponentially large space to find the right architecture, which requires multiple training runs on a large dataset. This has a prohibitive computational cost, as training each candidate architecture often requires millions of iterations.
Even after the right architecture with good accuracy is found, implementing it on a target hardware platform to meet latency and power constraints is not straightforward.
I will present a framework that efficiently utilizes reduced-precision computing to address the above challenges by considering the full stack of designing, training, and implementing the model on a target platform. This is achieved through careful analysis of the numerical instabilities associated with reduced-precision matrix operations, incorporation of a novel second-order, mixed-precision quantization approach, and a framework for hardware aware neural network design.
Bio: Amir Gholami is a postdoctoral research fellow in BAIR Lab at UC Berkeley. He received his PhD in Computational Science and Engineering Mathematics from UT Austin, working with Prof. George Biros on bio-physics based image analysis, a research topic which received UT Austin’s best doctoral dissertation award in 2018. Amir has extensive experience in High Performance Computing, second-order optimization methods, image registration, and large scale inverse problems, developing codes that have been scaled up to 200K cores. He is a Melosh Medal finalist, recipient of best student paper award in SC'17, Gold Medal in the ACM Student Research Competition in 2015, as well as best student paper finalist in SC’14.
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