| 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 | 299 | 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, 389-400, 2018 | 257 | 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 80, 2018 | 152 | 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 | 145 | 2021 |
| 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'2021, 1051-1063, 2021 | 125 | 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 FPGA'2020, 2020 | 120 | 2020 |
| AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks Y Fu, W Chen, H Wang, H Li, Y Lin, Z Wang ICML'2020, 2020 | 111 | 2020 |
| 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 NeurIPS'2019, 5139-5151, 2019 | 104 | 2019 |
| ShiftAddNet: A Hardware-Inspired Deep Network H You, X Chen, Y Zhang, C Li, S Li, Z Liu, Z Wang, Y Lin NeurIPS'2020, 2020 | 99 | 2020 |
| 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 | 96 | 2020 |
| PredictiveNet: An energy-efficient convolutional neural network via zero prediction NS Yingyan Lin, Charbel Sakr, Yongjune Kim ISCAS'2017, 2017 | 93* | 2017 |
| 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 ISCA'2020, 2020 | 91 | 2020 |
| Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? Y Fu, S Zhang, S Wu, C Wan, Y Lin ICLR'2022, 2022 | 90 | 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 | 86 | 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 | 82 | 2022 |
| ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design H You, Z Sun, H Shi, Z Yu, Y Zhao, Y Zhang, C Li, B Li, Y Lin HPCA'2023, 2022 | 72 | 2022 |
| GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models Y Fu, Y Zhang, Z Yu, S Li, Z Ye, C Li, C Wan, YC Lin ICCAD'2023, 2023 | 65 | 2023 |
| 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 | 64 | 2021 |
| RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering C Li, S Li, Y Zhao, W Zhu, Y Lin ICCAD'2022, 2022 | 56 | 2022 |
| 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 | 55 | 2020 |
Yonggan FuGeorgia Institute of Technologygatech.edu의 이메일 확인됨
