| CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices C Ding, S Liao, Y Wang, Z Li, N Liu, Y Zhuo, C Wang, X Qian, Y Bai, ... Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 343 | 2017 |
| PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning W Niu, X Ma, S Lin, S Wang, X Qian, X Lin, Y Wang, B Ren Proceedings of the Twenty-Fifth International Conference on Architectural …, 2020 | 296 | 2020 |
| AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates. N Liu, X Ma, Z Xu, Y Wang, J Tang, J Ye AAAI, 4876-4883, 2020 | 237 | 2020 |
| PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices. X Ma, FM Guo, W Niu, X Lin, J Tang, K Ma, B Ren, Y Wang AAAI, 5117-5124, 2020 | 213 | 2020 |
| SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning Z Kong, P Dong, X Ma, X Meng, W Niu, M Sun, X Shen, G Yuan, B Ren, ... European Conference on Computer Vision (ECCV), 620-640, 2022 | 191 | 2022 |
| StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs T Zhang, S Ye, X Feng, X Ma, K Zhang, Z Li, J Tang, S Liu, X Lin, Y Liu, ... IEEE Transactions on Neural Networks and Learning Systems, 2021 | 162* | 2021 |
| Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform? X Ma, S Lin, S Ye, Z He, L Zhang, G Yuan, SH Tan, Z Li, D Fan, X Qian, ... IEEE transactions on neural networks and learning systems, 2021 | 134* | 2021 |
| Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ... arXiv preprint arXiv:1903.09769, 2019 | 111* | 2019 |
| MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge G Yuan, X Ma, W Niu, Z Li, Z Kong, N Liu, Y Gong, Z Zhan, C He, Q Jin, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 96 | 2021 |
| CHEX: CHannel EXploration for CNN Model Compression Z Hou, M Qin, F Sun, X Ma, K Yuan, Y Xu, YK Chen, R Jin, Y Xie, SY Kung IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12287 …, 2022 | 92 | 2022 |
| Film-qnn: Efficient fpga acceleration of deep neural networks with intra-layer, mixed-precision quantization M Sun, Z Li, A Lu, Y Li, SE Chang, X Ma, X Lin, Z Fang Proceedings of the 2022 ACM/SIGDA International Symposium on Field …, 2022 | 76 | 2022 |
| FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator G Yuan, P Behnam, Z Li, A Shafiee, S Lin, X Ma, H Liu, X Qian, ... Proceedings of the 48th International Symposium on Computer Architecture …, 2021 | 76 | 2021 |
| Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? X Ma, G Yuan, X Shen, T Chen, X Chen, X Chen, N Liu, M Qin, S Liu, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 65 | 2021 |
| PIM-prune: Fine-grain DCNN pruning for crossbar-based process-in-memory architecture C Chu, Y Wang, Y Zhao, X Ma, S Ye, Y Hong, X Liang, Y Han, L Jiang 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 62 | 2020 |
| Tiny but accurate: A pruned, quantized and optimized memristor crossbar framework for ultra efficient dnn implementation X Ma, G Yuan, S Lin, C Ding, F Yu, T Liu, W Wen, X Chen, Y Wang 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), 301-306, 2020 | 60 | 2020 |
| An ultra-efficient memristor-based DNN framework with structured weight pruning and quantization using ADMM G Yuan, X Ma, C Ding, S Lin, T Zhang, ZS Jalali, Y Zhao, L Jiang, ... 2019 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2019 | 56 | 2019 |
| Vaqf: Fully automatic software-hardware co-design framework for low-bit vision transformer M Sun, H Ma, G Kang, Y Jiang, T Chen, X Ma, Z Wang, Y Wang arXiv preprint arXiv:2201.06618, 2022 | 53 | 2022 |
| ResNet Can Be Pruned 60×: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning X Ma, G Yuan, S Lin, Z Li, H Sun, Y Wang 2019 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 1-2, 2019 | 46 | 2019 |
| Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity L Yin, Y Wu, Z Zhang, CY Hsieh, Y Wang, Y Jia, M Pechenizkiy, Y Liang, ... The Forty-first International Conference on Machine Learning (ICML 2024), 2024 | 45 | 2024 |
| Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework Y Wang, C Ding, Z Li, G Yuan, S Liao, X Ma, B Yuan, X Qian, J Tang, ... Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018 | 45 | 2018 |
Yanzhi WangNortheastern University在 northeastern.edu 的電子郵件地址已通過驗證
