| 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 33 (9), 4930-4944, 2021 | 96 | 2021 |
| 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 34, 20838-20850, 2021 | 79 | 2021 |
| 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, ... 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture …, 2021 | 63 | 2021 |
| Rtmobile: Beyond real-time mobile acceleration of rnns for speech recognition P Dong, S Wang, W Niu, C Zhang, S Lin, Z Li, Y Gong, B Ren, X Lin, ... 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 56 | 2020 |
| 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 | 54 | 2019 |
| 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 | 48 | 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 | 41 | 2019 |
| 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 33 (5), 2259-2273, 2021 | 36 | 2021 |
| Auto-vit-acc: An fpga-aware automatic acceleration framework for vision transformer with mixed-scheme quantization Z Li, M Sun, A Lu, H Ma, G Yuan, Y Xie, H Tang, Y Li, M Leeser, Z Wang, ... 2022 32nd International Conference on Field-Programmable Logic and …, 2022 | 35 | 2022 |
| F8net: Fixed-point 8-bit only multiplication for network quantization Q Jin, J Ren, R Zhuang, S Hanumante, Z Li, Z Chen, Y Wang, K Yang, ... arXiv preprint arXiv:2202.05239, 2022 | 33 | 2022 |
| Improving dnn fault tolerance using weight pruning and differential crossbar mapping for reram-based edge ai G Yuan, Z Liao, X Ma, Y Cai, Z Kong, X Shen, J Fu, Z Li, C Zhang, H Peng, ... 2021 22nd International Symposium on Quality Electronic Design (ISQED), 135-141, 2021 | 32 | 2021 |
| Efficient transformer-based large scale language representations using hardware-friendly block structured pruning B Li, Z Kong, T Zhang, J Li, Z Li, H Liu, C Ding arXiv preprint arXiv:2009.08065, 2020 | 32 | 2020 |
| Tinyadc: Peripheral circuit-aware weight pruning framework for mixed-signal dnn accelerators G Yuan, P Behnam, Y Cai, A Shafiee, J Fu, Z Liao, Z Li, X Ma, J Deng, ... 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 926-931, 2021 | 27 | 2021 |
| Npas: A compiler-aware framework of unified network pruning and architecture search for beyond real-time mobile acceleration Z Li, G Yuan, W Niu, P Zhao, Y Li, Y Cai, X Shen, Z Zhan, Z Kong, Q Jin, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 27 | 2021 |
| A privacy-preserving-oriented dnn pruning and mobile acceleration framework Y Gong, Z Zhan, Z Li, W Niu, X Ma, W Wang, B Ren, C Ding, X Lin, X Xu, ... Proceedings of the 2020 on Great Lakes Symposium on VLSI, 119-124, 2020 | 23 | 2020 |
| Heatvit: Hardware-efficient adaptive token pruning for vision transformers P Dong, M Sun, A Lu, Y Xie, K Liu, Z Kong, X Meng, Z Li, X Lin, Z Fang, ... 2023 IEEE International Symposium on High-Performance Computer Architecture …, 2023 | 18 | 2023 |
| Grim: A general, real-time deep learning inference framework for mobile devices based on fine-grained structured weight sparsity W Niu, Z Li, X Ma, P Dong, G Zhou, X Qian, X Lin, Y Wang, B Ren IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6224 …, 2021 | 18 | 2021 |
| SS-Auto: A single-shot, automatic structured weight pruning framework of DNNs with ultra-high efficiency Z Li, Y Gong, X Ma, S Liu, M Sun, Z Zhan, Z Kong, G Yuan, Y Wang arXiv preprint arXiv:2001.08839, 2020 | 18 | 2020 |
| Non-structured dnn weight pruning considered harmful Y Wang, S Ye, Z He, X Ma, L Zhang, S Lin, G Yuan, SH Tan, Z Li, D Fan, ... arXiv preprint arXiv:1907.02124 2, 2019 | 14 | 2019 |
| Blk-rew: A unified block-based dnn pruning framework using reweighted regularization method X Ma, Z Li, Y Gong, T Zhang, W Niu, Z Zhan, P Zhao, J Tang, X Lin, B Ren, ... arXiv preprint arXiv:2001.08357, 2020 | 13 | 2020 |
Yanzhi WangNortheastern University在 northeastern.edu 的电子邮件经过验证
