Publications

 

• GAN Compression: Learning Efficient Architectures for Conditional GANs 
  M. Li, J. Lin, Y. Ding, Z. Liu, J. Zhu, S. Han
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  [paper][demo]
       
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
  T. Wang, K. Wang, H. Cai, J. Lin, Z. Liu, S. Han
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
• Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
  H. Wang, K. Wang, J. Yang, L. Shen, N. Sun, H.S. Lee and S. Han
  Design Automation Conference (DAC), 2020.
  [paper]
  
• SpArch: Efficient Architecture for Sparse Matrix Multiplication
  Z. Zhang, H. Wang, S. Han, W.J. Dally
  International Symposium on High-Performance Computer Architecture (HPCA), 2020.
  [paper][slides][website][2min talk][full talk]
  

• Once For All: Train One Network and Specialize It for Efficient Deployment
  H. Cai, C. Gan, T. Wang, Z. Zhang, S. Han
  International Conference on Learning Representations (ICLR), 2020.
  SysML workshop, 2020
  [paper][code][poster]
  

  

• Lite Transformer with Long Short Term Attention
  Z. Wu, Z. Liu, J. Lin, Y. Lin, S. Han
  International Conference on Learning Representations (ICLR), 2020.
  [paper]
  
• Point Voxel CNN for Efficient 3D Deep Learning
  Z. Liu, H. Tang, Y. Lin, S. Han
  Neural Information Processing System (NeurIPS), 2019. Spotlight presentation.
  [paper][deployment on MIT Driverless][playlist][talk][slides][code][website]
    
• Deep Leakage from Gradients
  L. Zhu, Z. Liu, S. Han
  Neural Information Processing System (NeurIPS), 2019.
  [paper]
  
• TSM: Temporal Shift Module for Efficient Video Understanding
  J. Lin, C. Gan, S. Han.
  International Conference on Computer Vision (ICCV), 2019.
  [paper][demo][code][poster][industry integration][MIT News][Engadget][MIT Technology Review][NVIDIA News][NVIDIA Jetson Developer Forum]
  
• HAQ: Hardware-Aware Automated Quantization with Mixed Precision
  K. Wang, Z. Liu, Y. Lin, J. Lin, S. Han.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. Oral presentation.
  [paper][slides][poster][code][video][BibTex]
  
• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
  H. Cai, L. Zhu, S. Han
  International Conference on Learning Representations (ICLR), 2019.
  [paper][code][demo][poster][MIT news][IEEE Spectrum][BibTex]
  
• Defensive Quantization: When Efficiency Meets Robustness
  J. Lin, C. Gan, S. Han
  International Conference on Learning Representations (ICLR), 2019.
  [paper][poster][MIT News][BibTex]
  
• AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
  Y. He, J. Lin, Z. Liu, H. Wang, J. Li, S. Han
  European Conference on Computer Vision (ECCV), 2018
  [paper][poster][website][code][BibTex]
  

Past publications at Stanford:

• Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
  Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally
  International Conference on Learning Representations (ICLR), April 2018.
• Efficient Sparse-winograd Convolutional Neural Networks
  Xingyu Liu, Jeff Pool, Song Han, William J. Dally
  International Conference on Learning Representations (ICLR), April 2018.
• DSD: Dense-Sparse-Dense Training for Deep Neural Networks
  Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Shijian Tang, Erich Elsen, Bryan Catanzaro, John Tran, William J. Dally
  International Conference on Learning Representations (ICLR), April 2017.
• Trained Tenary Quantization
  Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally
  International Conference on Learning Representations (ICLR), April 2017.
• Software-Hardware Co-Design for Efficient Neural Network Acceleration
  Kaiyuan Guo, Song Han, Song Yao, Yu Wang, Yuan Xie, Huazhong Yang
  Hot Chips special issue of IEEE Micro, March/April 2017
• ESE: Efficient Speech Recognition Engine for Sparse LSTM on FPGA
  Song Han, Junlong Kang, Huizi Mao, Yubin Li, Dongliang Xie, Hong Luo, Yu Wang, Huazhong Yang, William J. Dally
  NIPS workshop on Efficient Methods for Deep Neural Networks (EMDNN), Dec 2016, Best Paper Honorable Mention.
  International Symposium on Field-Programmable Gate Arrays (FPGA), Feb 2017, Best Paper Award.
• Compressing and Regularizing Deep Neural Networks, Improving Prediction Accuracy Using Deep Compression and DSD Training
  Song Han
  O’Reilly, Nov 2016.
• EIE: Efficient Inference Engine on Compressed Deep Neural Network
  Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark Horowitz, William J. Dally
  International Symposium on Computer Architecture (ISCA), June 2016; Hotchips, Aug 2016.
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
  Song Han, Huizi Mao, William J. Dally
  NIPS Deep Learning Symposium, December 2015.
  International Conference on Learning Representations (ICLR), May 2016, Best Paper Award.
• Learning both Weights and Connections for Efficient Neural Networks
  Song Han, Jeff Pool, John Tran, William J. Dally
  Advances in Neural Information Processing Systems (NIPS), December 2015.
• SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < 0.5MB Model Size
  Forrest Iandola, Song Han, Matthew Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
  arXiv 2016.
• Angel-Eye: A Complete Design Flow for Mapping CNN onto Customized Hardware
  Kaiyuan Guo, Lingzhi Sui, Jiantao Qiu, Song Yao, Song Han, Yu Wang, Huazhong Yang
  IEEE Computer Society Annual Symposium on VLSI (ISVLSI), July 2016.
• Hardware-friendly Convolutional Neural Network with Even-number Filter Size
  Song Yao, Song Han, Kaiyuan Guo, Jianqiao Wangni, Yu Wang, William J. Dally
  International Conference on Learning Representations Workshop, May 2016.

Services

• On-device Intelligence Workshop at MLSys’20, Organizing Committee
• ICCAD’19, Program Committee
• ICLR’19, Area Chair
• HPCA’18, Program Committee

Invited Talks

• Bandwidth-Efficient Deep Learning with Algorithm and Hardware Co-Design
  • ISSCC’19 forum of “Intelligence at the Edge: How Can We Make Machine Learning More Energy Efficient?”, Feb 2019
• Hardware-centric AutoML: Design Automation for Efficient Deep Learning Computing
  • Facebook, Feb 2019
  • Xilinx, Feb 2019
  • Intel, Feb 2019
  • Samsung, Feb 2019
  • NVIDIA, Feb 2019
  • SONY, Feb 2019
  • MediaTek, Jan 2019
  • TSMC, Jan 2019
  • Samsung, Dec 2018
  • SenseTime, Dec 2018
  • Google, Dec 2018

• Bandwidth-Efficient Deep Learning on Edge Devices
  • SONY, San Jose, November 2017
  • Renesas, Santa Clara, November 2017
  • Qualcomm, San Diego, December 2017
  • CloudMinds, Santa Clara, December 2017
  • Samsung AI Summit, Mountain View, January 2017

• Efficient Methods and Hardware for Deep Learning
  • Faculty interview, MIT, Princeton, UC Berkely, UT Austin, etc., March 2017
  • PhD thesis defense, Stanford, June 2017

• Deep Learning – Tutorial and Recent Trends
  • Conference tutorial at FPGA’17, Monterey. [slides]

• Deep Compression: A Deep Neural Network Compression Pipeline
  • Conference talk at ICLR, Puerto Rico, May 2016.
  • GPU Technology Conference (GTC), San Jose, March 2016.
  • Google, Mountain View, March 2015.
  • Stanford Computer System Colloquium, January 2016.

• EIE: Efficient Inference Engine on Compressed Deep Neural Network
  • Conference talk at ISCA, Korea, June 2016.
  • Movidius, San Mateo, April 2016.
  • HP Labs, Palo Alto, February 2016.
  • Apple, Cupertino, December 2015.
  • Stanford SystemX Fall Conference, Stanford, November 2015.

• Techniques for Efficient Implementation of Deep Neural Networks
  • Embedded Vision Alliance Member Meeting, March 2016.

• Deep Compression, DSD Training and EIE: Deep Neural Network Model Compression, Regularization and Hardware Acceleration
  • O’Reilly Artificial Intelligence Conference, New York, Sep 2016.
  • Facebook, Menlo Park, Aug 2016.
  • Tesla, Palo Alto, Aug 2016.
  • Xilinx, Santa Clara, Aug 2016.
  • OpenAI, San Francisco, Aug 2016. Video.
  • Microsoft Research, Redmond, June 2016.
  • Apple, Cupertino, June 2016.

Efficient AI on the edge, auto AI; model compression, gradient compression, compact model design, sparsity, auto pruning,  auto quantization, neural architecture search, efficient video recognition, efficient 3D point cloud, efficient transformer, specialized model, sparse hardware accelerator, accelerating compressed NN, FPGA.