| Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding S Han, H Mao, WJ Dally arXiv preprint arXiv:1510.00149, 2015 | 11379 | 2015 |
| EIE: Efficient inference engine on compressed deep neural network S Han, X Liu, H Mao, J Pu, A Pedram, MA Horowitz, WJ Dally ACM SIGARCH Computer Architecture News 44 (3), 243-254, 2016 | 3359 | 2016 |
| Deep gradient compression: Reducing the communication bandwidth for distributed training Y Lin, S Han, H Mao, Y Wang, WJ Dally arXiv preprint arXiv:1712.01887, 2017 | 1710 | 2017 |
| Trained ternary quantization C Zhu, S Han, H Mao, WJ Dally arXiv preprint arXiv:1612.01064, 2016 | 1366 | 2016 |
| Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation Z Liu, H Tang, A Amini, X Yang, H Mao, DL Rus, S Han 2023 IEEE international conference on robotics and automation (ICRA), 2774-2781, 2023 | 948 | 2023 |
| ESE: Efficient speech recognition engine with sparse lstm on fpga S Han, J Kang, H Mao, Y Hu, X Li, Y Li, D Xie, H Luo, S Yao, Y Wang, ... Proceedings of the 2017 ACM/SIGDA International Symposium on Field …, 2017 | 865 | 2017 |
| Exploring the granularity of sparsity in convolutional neural networks H Mao, S Han, J Pool, W Li, X Liu, Y Wang, WJ Dally Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 524* | 2017 |
| Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv 2015 S Han, H Mao, WJ Dally arXiv preprint arXiv:1510.00149, 0 | 455 | |
| DSD: Regularizing deep neural networks with dense-sparse-dense training flow S Han, J Pool, S Narang, H Mao, S Tang, E Elsen, B Catanzaro, J Tran, ... arXiv preprint arXiv:1607.04381, 2016 | 351* | 2016 |
| Vila: On pre-training for visual language models J Lin, H Yin, W Ping, P Molchanov, M Shoeybi, S Han Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2024 | 298 | 2024 |
| Towards Real-Time Object Detection on Embedded Systems Huizi Mao, Song Yao, Tianqi Tang, Boxun Li, Jun Yao, Yu Wang IEEE Transactions on Emerging Topics in Computing 99 (99), 1-1, 2016 | 105* | 2016 |
| Deep compression and EIE: Efficient inference engine on compressed deep neural network. S Han, X Liu, H Mao, J Pu, A Pedram, M Horowitz, B Dally Hot Chips Symposium, 1-6, 2016 | 64 | 2016 |
| A Delay Metric for Video Object Detection: What Average Precision Fails to Tell H Mao, X Yang, WJ Dally 2019 International Conference on Computer Vision (ICCV), 2019 | 57 | 2019 |
| Deep compression: Compressing deep neural network with pruning S Han, H Mao, WJ Dally Trained Quantization and Huffman Coding. arXiv 1510, v5, 2015 | 43 | 2015 |
| Real-time object detection towards high power efficiency J Yu, K Guo, Y Hu, X Ning, J Qiu, H Mao, S Yao, T Tang, B Li, Y Wang, ... 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 704-708, 2018 | 32 | 2018 |
| CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video H Mao, T Kong, WJ Dally 2019 The Conference on Systems and Machine Learning (SysML), 2019 | 29 | 2019 |
| Rebooting computing and low-power image recognition challenge YH Lu, AM Kadin, AC Berg, TM Conte, EP DeBenedictis, R Garg, ... 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 927-932, 2015 | 23 | 2015 |
| PatchNet--Short-range Template Matching for Efficient Video Processing H Mao, S Zhu, S Han, WJ Dally arXiv preprint arXiv:2103.07371, 2021 | 12 | 2021 |
| Retrospective: EIE: efficient inference engine on sparse and compressed neural network S Han, X Liu, H Mao, J Pu, A Pedram, MA Horowitz, WJ Dally arXiv preprint arXiv:2306.09552, 2023 | 5 | 2023 |
| Methods and Metrics for Efficient Video Object Detection H Mao Stanford University, 2021 | | 2021 |
Song HanMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu