Song Han is a fifth-year Ph.D. student with Prof. Bill Dally at Stanford University. His research focuses on energy-efficient deep learning computing at the intersection between machine learning and computer architecture. He proposed deep compression that can compress state-of-the-art CNNs by 10-49x while fully preserving prediction accuracy. Song designed EIE: Efficient Inference Engine, a hardware accelerator that can make inference directly on the compressed sparse model, which gives significant speedup and energy saving. His work has been covered by TheNextPlatform, TechEmergence, Embedded Vision, and O'Reilly. His work received the Best Paper Award in ICLR'16, Best Poster Award in Stanford Cloud Workshop'16, and Best Paper Honorable Mention in NIPS'16 EMDNN workshop. Before joining Stanford, Song graduated from Tsinghua University.