Song Han

Assistant Professor, MIT EECS

Efficient AI

with Tiny Resource

Accelerate Deep Learning Computing


Song Han is an assistant professor at MIT’s EECS. He received his PhD degree from Stanford University. His research focuses on efficient deep learning computing. He proposed “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, WiredQualcomm NewsVentureBeatIEEE Spectrum, integrated in 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 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” and the IEEE “AIs 10 to Watch: The Future of AI” award.

Google Scholar, YouTube, Twitter,Github, LinkedIn, Group Website

Research Interests

TinyML, putting AI on a diet, efficient algorithms and hardware for computation-intensive AI applications. 

We actively collaborate with industry partners. Many research projects have successfully influenced industry products. Welcome to drop me an email for collaboration.

Model Compression / AutoML / NAS: [MLSys’21][NeurIPS’20, spotlight][NeurIPS’20][ICLR’20][CVPR’20][CVPR’20][ICLR’19][CVPR’19, oral][ECCV’18][ICLR’16, BP][NIPS’15]
Efficient AI on edge devices: Video / Point Cloud / NLP / GAN: [NeurIPS’20][ACL’20][CVPR’20][ECCV’20][ICLR’20][NeurIPS’19, spotlight][ICCV’19]
HW for ML: [HPCA’21][HPCA’20][FPGA’17, BP][ISCA’16]
ML for HW: [DAC’21][DAC’20][NeurIPS’19 W]
Efficiency and privacy: [ECCV’20][NeurIPS’19](ICLR’19)


  • IEEE “AIs 10 to Watch: The Future of AI” Award, 2020
  • NSF CAREER Award, 2020
  • NVIDIA Academic Partnership Award, 2020
  • MIT Technology Review list of 35 Innovators Under 35, 2019
  • SONY Faculty Award, 2017/2018/2020
  • Amazon Machine Learning Research Award, 2018/2019
  • Facebook Research Award, 2019
  • Best paper award, FPGA’2017
  • Best paper award, ICLR’2016

Competition Awards




Ph.D. Stanford University, advised by Prof. Bill Dally

B.S. Tsinghua University


Email: FirstnameLastname [at] mit [dot] edu

Email for PhD/intern applications:

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