Tianqi Chen, University of Washington
Oct 30, 2017, Mon, 14:00-15:30
Deep learning has become ubiquitous and indispensable. Part of this revolution has been fueled by scalable deep learning systems. In this talk, I am going to talk about TVM: a unified intermediate representation (IR) stack that will close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends. TVM is a novel framework that can: Represent and optimize the common deep learning computation workloads for CPUs, GPUs, and other specialized hardware; Automatically transform the computation graph to minimize memory utilization, optimize data layout and fuse computation patterns; Provide an end-to-end compilation from existing front-end frameworks down to bare-metal hardware. I will talk about the problems and chance of learning system research around TVM.
Tianqi is a PhD student in University of Washington, working on machine learning and systems. He received his bachelor and master degrees from Shanghai Jiao Tong University. He is recipient of a Google PhD Fellowship in Machine Learning.