Song Han is an assistant professor in the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology (MIT). Dr. Han co-founded DeePhi Tech in 2016, a startup offering efficient solutions for deep learning computing (deep compression and hardware acceleration).

    Dr. Han's research focuses on energy-efficient deep learning, at the intersection between machine learning and computer architecture. He proposed Deep Compression that can compress deep neural networks by an order of magnitude without losing the prediction accuracy. He designed EIE: Efficient Inference Engine, a hardware accelerator that can perform inference directly on the compressed sparse model, which saves memory bandwidth and results in significant speedup and energy saving. His work has been featured by TheNextPlatform, TechEmergence, Embedded Vision and O’Reilly. His research efforts in model compression and hardware acceleration received the Best Paper Award at ICLR’16 and the Best Paper Award at FPGA’17, and these technologies also led to his startup DeePhi Tech. Before joining Stanford, Song graduated from Tsinghua University.