| Tackling the qubit mapping problem for NISQ-era quantum devices G Li, Y Ding, Y Xie Proceedings of the twenty-fourth international conference on architectural …, 2019 | 576 | 2019 |
| Rethinking the performance comparison between SNNS and ANNS L Deng, Y Wu, X Hu, L Liang, Y Ding, G Li, G Zhao, P Li, Y Xie Neural networks 121, 294-307, 2020 | 293 | 2020 |
| Yinyang k-means: A drop-in replacement of the classic k-means with consistent speedup Y Ding, Y Zhao, X Shen, M Musuvathi, T Mytkowicz International conference on machine learning, 579-587, 2015 | 186 | 2015 |
| Tianjic: A unified and scalable chip bridging spike-based and continuous neural computation L Deng, G Wang, G Li, S Li, L Liang, M Zhu, Y Wu, Z Yang, Z Zou, J Pei, ... IEEE Journal of Solid-State Circuits 55 (8), 2228-2246, 2020 | 163 | 2020 |
| {GNNAdvisor}: An adaptive and efficient runtime system for {GNN} acceleration on {GPUs} Y Wang, B Feng, G Li, S Li, L Deng, Y Xie, Y Ding 15th USENIX symposium on operating systems design and implementation (OSDI …, 2021 | 162 | 2021 |
| Deepsniffer: A dnn model extraction framework based on learning architectural hints X Hu, L Liang, S Li, L Deng, P Zuo, Y Ji, X Xie, Y Ding, C Liu, T Sherwood, ... Proceedings of the Twenty-Fifth International Conference on Architectural …, 2020 | 153 | 2020 |
| Autotuning algorithmic choice for input sensitivity Y Ding, J Ansel, K Veeramachaneni, X Shen, UM O’Reilly, ... ACM SIGPLAN Notices 50 (6), 379-390, 2015 | 145 | 2015 |
| Projection-based runtime assertions for testing and debugging quantum programs G Li, L Zhou, N Yu, Y Ding, M Ying, Y Xie Proceedings of the ACM on Programming Languages 4 (OOPSLA), 1-29, 2020 | 111 | 2020 |
| iPIM: Programmable in-memory image processing accelerator using near-bank architecture P Gu, X Xie, Y Ding, G Chen, W Zhang, D Niu, Y Xie 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 87 | 2020 |
| A 28nm 29.2 TFLOPS/W BF16 and 36.5 TOPS/W INT8 reconfigurable digital CIM processor with unified FP/INT pipeline and bitwise in-memory booth multiplication for cloud deep … F Tu, Y Wang, Z Wu, L Liang, Y Ding, B Kim, L Liu, S Wei, Y Xie, S Yin 2022 IEEE International Solid-State Circuits Conference (ISSCC) 65, 1-3, 2022 | 83 | 2022 |
| Comprehensive snn compression using admm optimization and activity regularization L Deng, Y Wu, Y Hu, L Liang, G Li, X Hu, Y Ding, P Li, Y Xie IEEE transactions on neural networks and learning systems 34 (6), 2791-2805, 2021 | 74 | 2021 |
| Towards efficient superconducting quantum processor architecture design G Li, Y Ding, Y Xie Proceedings of the Twenty-Fifth International Conference on Architectural …, 2020 | 74 | 2020 |
| Paulihedral: a generalized block-wise compiler optimization framework for quantum simulation kernels G Li, A Wu, Y Shi, A Javadi-Abhari, Y Ding, Y Xie Proceedings of the 27th ACM International Conference on Architectural …, 2022 | 73 | 2022 |
| Exploring adversarial attack in spiking neural networks with spike-compatible gradient L Liang, X Hu, L Deng, Y Wu, G Li, Y Ding, P Li, Y Xie IEEE transactions on neural networks and learning systems 34 (5), 2569-2583, 2021 | 71 | 2021 |
| Dota: detect and omit weak attentions for scalable transformer acceleration Z Qu, L Liu, F Tu, Z Chen, Y Ding, Y Xie Proceedings of the 27th ACM International Conference on Architectural …, 2022 | 66 | 2022 |
| Dynamic sparse graph for efficient deep learning L Liu, L Deng, X Hu, M Zhu, G Li, Y Ding, Y Xie arXiv preprint arXiv:1810.00859, 2018 | 66 | 2018 |
| ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences B Feng, L Qin, Z Zhang, Y Ding, S Chu Cryptology ePrint Archive, 2021 | 57 | 2021 |
| Sgquant: Squeezing the last bit on graph neural networks with specialized quantization B Feng, Y Wang, X Li, S Yang, X Peng, Y Ding 2020 IEEE 32nd international conference on tools with artificial …, 2020 | 54 | 2020 |
| Understanding gnn computational graph: A coordinated computation, io, and memory perspective H Zhang, Z Yu, G Dai, G Huang, Y Ding, Y Xie, Y Wang Proceedings of Machine Learning and Systems 4, 467-484, 2022 | 51 | 2022 |
| Rubik: A hierarchical architecture for efficient graph neural network training X Chen, Y Wang, X Xie, X Hu, A Basak, L Liang, M Yan, L Deng, Y Ding, ... IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2021 | 49 | 2021 |
