Publications

• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
  H. Wang, Y. Ding, J. Gu, Z. Li, Y. Lin, D. Pan, F. Chong, S. Han
  HPCA’22
  paper / qmlsys website / TorchQuantum / MIT News / video 

• QuEst: Graph Transformer for Quantum Circuit Reliability Prediction 
  H. Wang, P. Liu, J. Cheng, Z. Liang, J. Gu, Z. Li, Y. Ding, W. Jiang, Y. Shi, X. Qian, D. Z. Pan, F. T. Chong, S. Han
  ICCAD’22, invited

• Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
  L. Zhu, H. Lin, Y Lu, Y. Lin, S. Han
  NeurIPS’21
  paper / website / slides / poster

• PointAcc: Efficient Point Cloud Accelerator
  Y. Lin, Z. Zhang, H. Tang, H. Wang, S. Han
  MICRO’21
  paper / website / slides / talk / lightning talk

• LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision
  Z. Liu, S. Stent, J. Li, J. Gideon, S. Han
  ICCV’21
  paper / website

• SemAlign: Annotation-Free Camera-LiDAR Calibration with Semantic Alignment Loss
  Z. Liu, H. Tang, S. Zhu, S. Han
  IROS’21
  paper / website

• NAAS: Neural Accelerator Architecture Search
  Y. Lin, M. Yang, S. Han
  DAC’21
  paper / website / slides / video

• Anycost GANs for Interactive Image Synthesis and Editing
  J. Lin, R. Zhang, F. Ganz, S. Han, J. Zhu
  CVPR’21
  website / video / code

• Efficient and Robust LiDAR-Based End-to-End Navigation
  Z Liu, A. Amini, S. Zhu, S. Karaman, S. Han, D. Rus
  ICRA’21
  website / video / news

• IOS: Inter-Operator Scheduler For CNN Acceleration
  Y. Ding, L. Zhu, Z. Jia, G. Pekhimenko, S. Han
  MLSys’21
  code / video / slides / poster

• SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
  H. Wang, Z. Zhang, S. Han
  HPCA’21
  Paper  /  Slides  /  Intro Video  /  Short Video  /  Project Page

• MCUNet: Tiny Deep Learning on IoT Devices
  J. Lin, W. Chen, Y. Lin, J. Cohn, C. Gan, S. Han
  NeurIPS’20, spotlight 
  MIT News / website / Wired / Stacey on IoT / Morning Brew / IBM / Analytics Insight

• Tiny Transfer Learning: Reduce Activations, not Trainable Parameters for Efficient On-Device Learning
  H. Cai, C. Gan, L. Zhu, S. Han
  NeurIPS’20 
  website / slides / code

• Differentiable Augmentation for Data-Efficient GAN Training  
  S. Zhao, Z. Liu, J. Lin, J. Zhu, S. Han
  NeurIPS’20
  code / website / talk / VentureBeat / blog

• Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution  
  H. Tang∗, Z. Liu∗, S. Zhao, Y. Lin, J. Lin. H. Wang, S. Han
  ECCV’20
  [website][video][tutorial][code][TorchSparse]

• HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

  H. Wang, Z. Wu, Z. Liu, H. Cai, L. Zhu, C Gan, S. Han
  ACL’20
  Paper  /  Slides  /  Video  /  Code  /  Project Page

• GAN Compression: Learning Efficient Architectures for Conditional GANs 
  M. Li, J. Lin, Y. Ding, Z. Liu, J. Zhu, S. Han
  CVPR’20
  paper / website / demo / tutorial 

• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy

  T. Wang, K. Wang, H. Cai, J. Lin, Z. Liu, S. Han
  CVPR’20
  Code  /  Video

• GCN-RL Circuit Designer: 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  /  Slides  /  Poster  /  Video  /  Project Page 

• 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  /  2-min Intro  /  Intro  /  Talk  /  Slides  /  Project Page
  

• 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.
  also appeared at TinyML summit, SysML workshop and CVPR workshop 2020.
  [paper][website][code][tutorial][poster][ICLR talk][tinyML talk][CVPR talk][tutorial][MIT news][Qualcomm news][Venture Beat]
  

• 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][website][code][slides]
  
  
• Point Voxel CNN for Efficient 3D Deep Learning
  Z. Liu, H. Tang, Y. Lin, S. Han
  Neural Information Processing System (NeurIPS), 2019. Spotlight 
  [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][website][code][poster][colab]
  
  
• 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][MIT News][Engadget][MIT Technology Review][NVIDIA News][NVIDIA Jetson Developer Forum]
  [industry integration: @NVIDIA, @Baidu]
  
  
• 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]
  [industry integration: @AWS, @Facebook]
  
  
• 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.