Song Han is an assistant professor at MIT EECS. Dr. Han received the Ph.D. degree in Electrical Engineering from Stanford advised by Prof. Bill Dally. Dr. Han’s research focuses on efficient deep learning computing. He proposed “Deep Compression” and the hardware implementation “ Efficient Inference Engine” that impacted the industry. His work received the best paper award in ICLR’16 and FPGA’17. The pruning, compression and acceleration techniques have been integrated into many AI chip products. His hobbies include biking, snowboarding, drum sets, and design.

Research Interests

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 recognition, specialized model, specialized hardware, hardware acceleration, FPGA, neural network and hardware co-design.

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

News Blog

• Dec 2019: Once-For-All Network (OFA) is accepted by ICLR’2020. Train only once, specialize for many deployment scenarios. OFA decouples model training from architecture search. OFA consistently achieves better performance than SOTA models (MobileNet-v3, EfficientNet) while reducing orders of magnitude GPU hours and CO2 emission than NAS. Draft to be updated.
  
  
• Dec 2019: Efficient Transformer for Mobile Applications is accepted by ICLR’2020. We investigate the mobile setting for NLP tasks to facilitate the deployment of NLP model on the edge devices. Draft to be updated.

   

• Nov 2019: AutoML for Architecting Efficient and Specialized Neural Networks to appear at IEEE Micro.
• Nov 2019: SpArch: Efficient Architecture for Sparse Matrix Multiplication to appear at International Symposium on High-Performance Computer Architecture (HPCA) 2020.

   

• Oct 2019: TSM is featured by MIT News, Engadget, NVIDIA News, MIT Technology Review.

   

• Oct 2019: HAN Lab team received the first place in the Low Power Computer Vision Challenge, DSP track at ICCV’19 using the Once-for-all Network.
• Oct 2019: Our solution to the Visual Wake Words Challenge is highlighted by Google. The technique is ProxylessNAS.[demo][code].

   

• Oct 2019: Open source: the search code for ProxylessNAS is available on Github.

   

• Oct 2019: Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos is accepted by NeurIPS workshop on Systems for ML. TSM, a compact model for video understanding, is hardware-friendly not only for inference but also for training. With TSM, we can scale up Kinetics training to 1536 GPUs and reduce the training time from 2 days to 15 minutes. TSM is highlighted at the opening remarks at AI Research Week hosted by the MIT-IBM Watson AI Lab. [paper]

   

• Oct 2019: Distributed Training across the World is accepted by NeurIPS workshop on Systems for ML.

   

• Oct 2019: Neural-Hardware Architecture Search is accepted by NeurIPS workshop on ML for Systems.

   

• Sep 2019: Point-Voxel CNN for Efficient 3D Deep Learning is accepted by NeurIPS’19 as spotlight presentation. [paper]

   

• Sep 2019: Deep Leakage from Gradients is accepted by NeurIPS’19. [paper]

   

• July 2019: TSM: Temporal Shift Module for Efficient Video Understanding is accepted by ICCV’19. Video understanding is more computationally intensive than images, making it harder to deploy on edge devices. Frames in the temporal dimension is highly redundant. TSM uses 2D convolution’s computation complexity and achieves better temporal modeling ability than 3D convolution. TSM also enables low-latency, real-time video recognition (13ms latency on Jetson Nano and 70ms latency on Raspberry PI-3). [paper][demo][code][poster][industry integration][MIT News][Engadget][MIT Technology Review][NVIDIA News][NVIDIA Jetson Developer Forum]

   

• June 2019: HAN Lab team received the first place in the Visual Wake-up Word Challenge@CVPR’19. The task is human detection on IoT device that has a tight computation budget:  <250KB model size, <250KB peak memory usage, <60M MAC. The techniques are described in the ProxylessNAS paper. [code][Raspberry Pi and Pixel 3 demo]
• June 2019: HAN Lab team received the third place in the classification track of the LPIRC competition@CVPR. The task is to perform image classification within 30ms latency on a Pixel-2 phone while achieving higher accuracy. The techniques are described in the ProxylessNAS paper.
• June 2019: Song is presenting “Design Automation for Efficient Deep Learning by Hardware-aware Neural Architecture Search and Compression” at ICML workshop on On-Device Machine Learning& Compact Deep Neural Network Representations, CVPR workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications, CVPR workshop on Efficient Deep Learning for Computer Vision, UCLA, TI and

  Workshop on Approximate Computing Across the Stack. [paper][slides]

• June 2019: Open source. AMC: AutoML for Model Compression and Acceleration on Mobile Devices is available on Github. AMC uses reinforcement learning to automatically find the optimal sparsity ratio for channel pruning.
  
  
• June 2019: Open source. HAQ: Hardware-aware Automated Quantization with Mixed Precision is available on Github.
• May 2019: Song Han received Facebook Research Award.
• April 2019: Defensive Quantization on MIT News: Improving Security as Artificial Intelligence Moves to Smartphones.
• April 2019: Our manuscript of Design Automation for Efficient Deep Learning Computing is available on arXiv.[slides]

   

• March 2019: ProxylessNAS on MIT News: Kicking Neural Network Design Automation into High Gear and IEEE Spectrum: Using AI to Make Better AI.
   
• March 2019: HAQ: Hardware-aware Automated Quantization with Multi-precision is accepted by CVPR’19  as oral presentation. HAQ leverages reinforcement learning to automatically determine the quantization policy (bit width per layer), and we take the hardware accelerator’s feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback (both latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. 
  So far, ProxylessNAS [ICLR’19] => AMC [ECCV’18] => HAQ [CVPR’19] forms a pipeline of  efficient AutoML.
• Feb 2019: Song presented “Bandwidth-Efficient Deep Learning with Algorithm and Hardware Co-Design” at ISSCC’19 in the forum “Intelligence at the Edge: How Can We Make Machine Learning More Energy Efficient?

• Jan 2019: Song is appointed to the Robert J. Shillman (1974) Career Development Chair.

• Jan 2019: “Song Han: Democratizing artificial intelligence with deep compression” by MIT Industry Liaison Program. [article][video]

• Dec 2018: Congrats Xiangning received the 2nd place in the feedback phase of the NeuraIPS’18 AutoML Challenge: AutoML for Lifelong Machine Learning.

• Dec 2018: Our work on Defensive Quantization: When Efficiency Meets Robustness is accepted by ICLR’19. Neural network quantization is becoming an industry standard to compress and efficiently deploy deep learning models. Is model compression a free lunch? No, if not treated carefully. We observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people’s awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. [paper][MIT News]

• Dec 2018: Our work on Learning to Design Circuits appeared at NeurIPS workshop on Machine Learning for Systems. Analog IC design relies on human experts to search for parameters that satisfy circuit specifications with their experience and intuitions, which is highly labor intensive and time consuming. This paper propose a learning based approach to size the transistors and help engineers to shorten the design cycle. [paper]

• Dec 2018: Our work on ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware is accepted by ICLR’19. Neural Architecture Search (NAS) is computation intensive. ProxylessNAS saves the GPU hours by 200x than NAS, saves GPU memory by 10x than DARTS, while directly searching on ImageNet. ProxylessNAS is hardware-aware. It can design specialized neural network architecture for different hardware, making inference fast. With >74.5% top-1 accuracy, the measured latency of ProxylessNAS is 1.8x faster than MobileNet-v2, the current industry standard for mobile vision. [paper][code][demo][poster][MIT news][IEEE Spectrum]

• Sep 2018: Song Han received Amazon Machine Learning Research Award.

• Sep 2018: Song Han received SONY Faculty Award.

• Sep 2018: Our work on AMC: AutoML for Model Compression and Acceleration on Mobile Devices is accepted by ECCV’18. This paper proposes learning-based method to perform model compression, rather than relying on human heuristics and rule-based methods. AMC can automate the model compression process, achieve better compression ratio, and also be more sample efficient. It takes shorter time can do better than rule-based heuristics. AMC compresses ResNet-50 by 5x without losing accuracy. AMC makes MobileNet-v1 2x faster with 0.4% loss of accuracy. [paper / bibTeX]

• June 2018: Song presents invited paper “Bandwidth Efficient Deep Learning” at Design Automation Conference (DAC’18). The paper talks about techniques to save memory bandwidth, networking bandwidth, and engineer bandwidth for efficient deep learning.

• Mar 26, 2018: Song presented Deep Gradient Compression at NVIDIA GPU Technology Conference.

• Feb 26, 2018: Song presented “Bandwidth Efficient Deep Learning: Challenges and Trade-offs” at FPGA’18 panel session.

• Jan 29, 2018: Deep Gradient Compression is accepted by ICLR’18. This technique can reduce the communication bandwidth by 500x and improves the scalability of large-scale distributed training. [slides].

Education

• Ph.D. Stanford University, Sep. 2012 to Sep. 2017
• B.S. Tsinghua University, Aug. 2008 to Jul. 2012

Contact

• Email: FirstnameLastname [at] mit [dot] edu