Song Han is an assistant professor at MIT’s EECS. He received his Ph.D. degree from Stanford University. His research focuses on efficient deep learning computing. He proposed the “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His team’s work on hardware-aware neural architecture search (ProxylessNAS, Once-for-All Network (OFA), MCUNet) was highlighted by MIT News, Wired, Qualcomm News, VentureBeat, IEEE Spectrum, integrated into PyTorch and AutoGluon, received six low-power computer vision contest awards in flagship AI conferences, and a world-record in the open division of MLPerf inference benchmark (1.078M Img/s). Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award, NVIDIA Academic Partnership Award. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution of the “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning” and the IEEE “AIs 10 to Watch: The Future of AI” award.