| Accelerating transformer-based deep learning models on fpgas using column balanced block pruning H Peng, S Huang, T Geng, A Li, W Jiang, H Liu, S Wang, C Ding 2021 22nd International Symposium on Quality Electronic Design (ISQED), 142-148, 2021 | 96 | 2021 |
| A length adaptive algorithm-hardware co-design of transformer on fpga through sparse attention and dynamic pipelining H Peng*, S Huang*, S Chen, B Li, T Geng, A Li, W Jiang, W Wen, J Bi, ... Proceedings of the 59th ACM/IEEE Design Automation Conference, 1135-1140, 2022 | 49 | 2022 |
| Accommodating transformer onto fpga: Coupling the balanced model compression and fpga-implementation optimization P Qi, Y Song, H Peng, S Huang, Q Zhuge, EHM Sha Proceedings of the 2021 on Great Lakes Symposium on VLSI, 163-168, 2021 | 48 | 2021 |
| Accelerating framework of transformer by hardware design and model compression co-optimization P Qi, EHM Sha, Q Zhuge, H Peng, S Huang, Z Kong, Y Song, B Li 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 1-9, 2021 | 44 | 2021 |
| Et: re-thinking self-attention for transformer models on gpus S Chen*, S Huang*, S Pandey, B Li, GR Gao, L Zheng, C Ding, H Liu Proceedings of the international conference for high performance computing …, 2021 | 36 | 2021 |
| Accel-gcn: High-performance gpu accelerator design for graph convolution networks X Xie, H Peng, A Hasan, S Huang, J Zhao, H Fang, W Zhang, T Geng, ... 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 01-09, 2023 | 29 | 2023 |
| Autorep: Automatic relu replacement for fast private network inference H Peng*, S Huang*, T Zhou*, Y Luo, C Wang, Z Wang, J Zhao, X Xie, A Li, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 29 | 2023 |
| Sparse progressive distillation: Resolving overfitting under pretrain-and-finetune paradigm S Huang, D Xu, IEH Yen, S Chang, B Li, S Chen, M Xie, H Liu, C Ding Proceedings of the 60th Annual Meeting of the Association for Computational …, 2021 | 29 | 2021 |
| Maxk-gnn: Extremely fast gpu kernel design for accelerating graph neural networks training H Peng, X Xie, K Shivdikar, MA Hasan, J Zhao, S Huang, O Khan, D Kaeli, ... Proceedings of the 29th ACM International Conference on Architectural …, 2024 | 28 | 2024 |
| Towards sparsification of graph neural networks H Peng, D Gurevin, S Huang, T Geng, W Jiang, O Khan, C Ding 40th IEEE International Conference on Computer Design (ICCD), 2022 | 28 | 2022 |
| Lingcn: Structural linearized graph convolutional network for homomorphically encrypted inference H Peng, R Ran, Y Luo, J Zhao, S Huang, K Thorat, T Geng, C Wang, X Xu, ... Advances in Neural Information Processing Systems 36, 2024 | 23 | 2024 |
| An automatic and efficient BERT pruning for edge AI systems S Huang, N Liu, Y Liang, H Peng, H Li, D Xu, M Xie, C Ding 2022 23rd International Symposium on Quality Electronic Design (ISQED), 1-6, 2022 | 15 | 2022 |
| Rrnet: Towards relu-reduced neural network for two-party computation based private inference H Peng, S Zhou, Y Luo, N Xu, S Duan, R Ran, J Zhao, S Huang, X Xie, ... arXiv preprint arXiv:2302.02292, 2023 | 14 | 2023 |
| HMC-TRAN A Tensor-core Inspired Hierarchical Model Compression for Transformer-based DNNs on GPU S Huang, S Chen, H Peng, D Manu, Z Kong, G Yuan, L Yang, S Wang, ... Proceedings of the 2021 on Great Lakes Symposium on VLSI, 169-174, 2021 | 14* | 2021 |
| Dynamic sparse training via balancing the exploration-exploitation trade-off S Huang, B Lei, D Xu, H Peng, Y Sun, M Xie, C Ding 2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023 | 13 | 2023 |
| Codg-reram: An algorithm-hardware co-design to accelerate semi-structured gnns on reram Y Luo, P Behnam, K Thorat, Z Liu, H Peng, S Huang, S Zhou, O Khan, ... 2022 IEEE 40th International Conference on Computer Design (ICCD), 280-289, 2022 | 13 | 2022 |
| Co-exploration of graph neural network and network-on-chip design using automl D Manu, S Huang, C Ding, L Yang Proceedings of the 2021 on Great Lakes Symposium on VLSI, 175-180, 2021 | 11 | 2021 |
| MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training H Peng, X Xie, K Shivdikar, MD Hasan, J Zhao, S Huang, O Khan, D Kaeli, ... arXiv preprint arXiv:2312.08656, 2023 | 8 | 2023 |
| Analyzing and defending against membership inference attacks in natural language processing classification Y Wang, N Xu, S Huang, K Mahmood, D Guo, C Ding, W Wen, ... 2022 IEEE International Conference on Big Data (Big Data), 5823-5832, 2022 | 8 | 2022 |
| Neurogenesis dynamics-inspired spiking neural network training acceleration S Huang, H Fang, K Mahmood, B Lei, N Xu, B Lei, Y Sun, D Xu, W Wen, ... 2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023 | 6 | 2023 |
Caiwen DingAssociate Professor, University of Minnesota Twin Cities在 umn.edu 的电子邮件经过验证
