Song Han is an associate professor at MIT EECS. He received his PhD degree from Stanford University. He proposed the “Deep Compression” technique including pruning and quantization that is widely used for efficient AI computing, and “Efficient Inference Engine” that first brought weight sparsity to modern AI chips, which influenced NVIDIA’s Ampere GPU Architecture with Sparse Tensor Core. He pioneered the TinyML research that brings deep learning to IoT devices, enabling learning on the edge (appeared on MIT home page). His team’s work on hardware-aware neural architecture search (once-for-all network) enables users to design, optimize, shrink and deploy AI models to resource-constrained hardware devices, receiving the first place in many low-power computer vision contests in flagship AI conferences. Song received best paper awards at ICLR and FPGA, faculty awards from Amazon, Facebook, NVIDIA, Samsung and SONY. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “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”, IEEE “AIs 10 to Watch: The Future of AI” award, and Sloan Research Fellowship.
Song’s cutting-edge research in efficient AI computing has profoundly influenced the industry. He was the cofounder of DeePhi (now part of AMD), and cofounder of OmniML (now part of NVIDIA).
Our efficient ML research has influenced and landed in many industry products, thanks to the close collaboration with our sponsors: Intel OpenVino, Intel Neural Compressor, Apple Neural Engine, NVIDIA Sparse Tensor Core, NVIDIA FasterTransformer, AMD-Xilinx Vitis AI, Qualcomm AI Model Efficiency Toolkit (AIMET), Amazon AutoGluon, Microsoft NNI, SONY Neural Architecture Search Library, SONY Model Compression Toolkit, ADI MAX78000/MAX78002 Model Training and Synthesis Tool, Ford Trailer Backup Assist.
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