| Drawing early-bird tickets: Towards more efficient training of deep networks H You, C Li, P Xu, Y Fu, Y Wang, X Chen, RG Baraniuk, Z Wang, Y Lin ICLR'2020 Spotlight, 2020 | 261 | 2020 |
| On-demand deep model compression for mobile devices: A usage-driven model selection framework S Liu, Y Lin, Z Zhou, K Nan, H Liu, J Du MobiSys'2018: The International Conference on Mobile Systems, Applications …, 2018 | 237 | 2018 |
| Deep -Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions J Wu, Y Wang, Z Wu, Z Wang, A Veeraraghavan, Y Lin ICML'2018: Proceedings of the 35th International Conference on Machine …, 2018 | 144 | 2018 |
| HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark C Li, Z Yu, Y Fu, Y Zhang, Y Zhao, H You, Q Yu, Y Wang, Y Lin ICLR'2021 Spotlight, 2021 | 119 | 2021 |
| AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs P Xu, X Zhang, C Hao, Y Zhao, Y Zhang, Y Wang, C Li, D Chen, Y Lin 28th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays …, 2020 | 105 | 2020 |
| AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks Y Fu, W Chen, H Wang, H Li, Y Lin, Z Wang The 37th International Conference on Machine Learning (ICML'2020), 2020 | 103 | 2020 |
| I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization T Geng, C Wu, Y Zhang, C Tan, C Xie, H You, M Herbordt, Y Lin, A Li MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture …, 2021 | 92 | 2021 |
| PredictiveNet: An energy-efficient convolutional neural network via zero prediction NS Yingyan Lin, Charbel Sakr, Yongjune Kim IEEE International Symposium on Circuits and Systems (ISCAS'2017), 2017 | 90 | 2017 |
| E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings Y Wang, Z Jiang, X Chen, P Xu, Y Zhao, Y Lin, Z Wang Advances in Neural Information Processing Systems (NeurIPS'2019), 5139-5151, 2019 | 88 | 2019 |
| Dual dynamic inference: Enabling more efficient, adaptive, and controllable deep inference Y Wang, J Shen, TK Hu, P Xu, T Nguyen, R Baraniuk, Z Wang, Y Lin IEEE Journal of Selected Topics in Signal Processing 14 (4), 623-633, 2020 | 80 | 2020 |
| ShiftAddNet: A Hardware-Inspired Deep Network H You, X Chen, Y Zhang, C Li, S Li, Z Liu, Z Wang, Y Lin Advances in Neural Information Processing Systems (NeurIPS'2020), 2020 | 76 | 2020 |
| TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain W Li, P Xu, Y Zhao, H Li, Y Xie, Y Lin IEEE/ACM International Symposium on Computer Architecture (ISCA'2020), 2020 | 74 | 2020 |
| Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? Y Fu, S Zhang, S Wu, C Wan, Y Lin ICLR'2022, 2022 | 64 | 2022 |
| PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication C Wan, Y Li, CR Wolfe, A Kyrillidis, NS Kim, Y Lin ICLR'2022, 2022 | 57 | 2022 |
| BNS-GCN: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling C Wan, Y Li, A Li, NS Kim, Y Lin MLSys'2022, 2022 | 56 | 2022 |
| SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation Y Zhao, X Chen, Y Wang, C Li, H You, Y Fu, Y Xie, Z Wang, Y Lin IEEE/ACM International Symposium on Computer Architecture (ISCA'2020), 2020 | 49 | 2020 |
| DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures Y Zhao, C Li, Y Wang, P Xu, Y Zhang, Y Lin IEEE International Conference on Acoustics, Speech and Signal Processing …, 2020 | 49 | 2020 |
| Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference J Shen, Y Fu, Y Wang, P Xu, Z Wang, Y Lin The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI'2020), 2020 | 49 | 2020 |
| GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design H You, T Geng, Y Zhang, A Li, Y Lin HPCA'2022, 2021 | 42 | 2021 |
| Variation-tolerant architectures for convolutional neural networks in the near threshold voltage regime Y Lin, S Zhang, NR Shanbhag IEEE international workshop on signal processing systems (SiPS'2016), 17-22, 2016 | 37 | 2016 |
Yonggan FuGeorgia Institute of Technology在 gatech.edu 的电子邮件经过验证
