Keynote talk: Song Han

I am an assistant professor in EECS at MIT. I completed my Ph.D. at Stanford University advised by Prof. Bill Dally. I was a postdoctoral researcher at Google Brain before joining MIT. My research focuses on energy-efficient deep learning computing, at the intersection between machine learning and computer architecture. As Moore’s law is slowing down, we often need to first tweak the algorithm to be hardware friendly (e.g. Deep Compression that can compress deep neural-nets by 10-50x), then design the specialized hardware for a target domain (e.g. EIE: Efficient Inference Engine that directly perform NN inference on the sparse, compressed model). Combining algorithm and hardware, the design space becomes very large, so we need AI-assisted design automation (e.g. ProxylessNAS that automatically search the optimal neural-net architecture for target hardware architecture). Several techniques has been adopted by the industry.