| Machine learning for electronic design automation: A survey G Huang, J Hu, Y He, J Liu, M Ma, Z Shen, J Wu, Y Xu, H Zhang, K Zhong, ... ACM Transactions on Design Automation of Electronic Systems (TODAES) 26 (5 …, 2021 | 216 | 2021 |
| Neural network accelerator comparison K Guo, W Li, K Zhong, Z Zhu, S Zeng, S Han, Y Xie, P Debacker, ... NICS Lab of Tsinghua University. http://nicsefc. ee. tsinghua. edu. cn …, 2021 | 32 | 2021 |
| Enabling efficient and flexible FPGA virtualization for deep learning in the cloud S Zeng, G Dai, H Sun, K Zhong, G Ge, K Guo, Y Wang, H Yang 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom …, 2020 | 22 | 2020 |
| Exploring the potential of low-bit training of convolutional neural networks K Zhong, X Ning, G Dai, Z Zhu, T Zhao, S Zeng, Y Wang, H Yang IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2022 | 10 | 2022 |
| Towards lower bit multiplication for convolutional neural network training K Zhong, T Zhao, X Ning, S Zeng, K Guo, Y Wang, H Yang arXiv preprint arXiv:2006.02804 3 (4), 2020 | 9 | 2020 |
| Boolnet: minimizing the energy consumption of binary neural networks N Guo, J Bethge, H Yang, K Zhong, X Ning, C Meinel, Y Wang arXiv preprint arXiv:2106.06991, 2021 | 7 | 2021 |
| Llm-mq: Mixed-precision quantization for efficient llm deployment S Li, X Ning, K Hong, T Liu, L Wang, X Li, K Zhong, G Dai, H Yang, ... The Efficient Natural Language and Speech Processing Workshop with NeurIPS 9, 2023 | 6 | 2023 |
| CoGNN: An algorithm-hardware co-design approach to accelerate GNN inference with minibatch sampling K Zhong, S Zeng, W Hou, G Dai, Z Zhu, X Zhang, S Xiao, H Yang, Y Wang IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2023 | 5 | 2023 |
| Exploiting parallelism with vertex-clustering in processing-in-memory-based GCN accelerators Y Zhu, Z Zhu, G Dai, K Zhong, H Yang, Y Wang 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 652-657, 2022 | 4 | 2022 |
| A unified FPGA virtualization framework for general-purpose deep neural networks in the cloud S Zeng, G Dai, H Sun, J Liu, S Li, G Ge, K Zhong, K Guo, Y Wang, H Yang ACM Transactions on Reconfigurable Technology and Systems (TRETS) 15 (3), 1-31, 2021 | 4 | 2021 |
| An efficient accelerator for point-based and voxel-based point cloud neural networks X Yang, T Fu, G Dai, S Zeng, K Zhong, K Hong, Y Wang 2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023 | 3 | 2023 |
| NTGAT: A graph attention network accelerator with runtime node tailoring W Hou, K Zhong, S Zeng, G Dai, H Yang, Y Wang Proceedings of the 28th Asia and South Pacific Design Automation Conference …, 2023 | 2 | 2023 |
| Enable efficient and flexible fpga virtualization for deep learning in the cloud S Zeng, G Dai, K Zhong, H Sun, G Ge, K Guo, Y Wang, H Yang Proceedings of the 2020 ACM/SIGDA International Symposium on Field …, 2020 | 2 | 2020 |
| FEASTA: A Flexible and Efficient Accelerator for Sparse Tensor Algebra in Machine Learning K Zhong, Z Zhu, G Dai, H Wang, X Yang, H Zhang, J Si, Q Mao, S Zeng, ... Proceedings of the 29th ACM International Conference on Architectural …, 2024 | 1 | 2024 |
| BoolNet: Towards Energy-Efficient Binary Neural Networks Design and Optimization N Guo, J Bethge, H Yang, K Zhong, X Ning, C Meinel, Y Wang 2nd Workshop on Sustainable AI, 0 | | |
| BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps N Guo, J Bethge, H Yang, K Zhong, X Ning, C Meinel, Y Wang | | |
