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 | 292 | 2021 |
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 | 56 | 2022 |
Cogdl: A comprehensive library for graph deep learning Y Cen, Z Hou, Y Wang, Q Chen, Y Luo, Z Yu, H Zhang, X Yao, A Zeng, ... Proceedings of the ACM Web Conference 2023, 747-758, 2023 | 19 | 2023 |
Llmcompass: Enabling efficient hardware design for large language model inference H Zhang, A Ning, RB Prabhakar, D Wentzlaff 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture …, 2024 | 16* | 2024 |
Heuristic adaptability to input dynamics for spmm on gpus G Dai, G Huang, S Yang, Z Yu, H Zhang, Y Ding, Y Xie, H Yang, Y Wang Proceedings of the 59th ACM/IEEE Design Automation Conference, 595-600, 2022 | 16 | 2022 |
Hypergef: A framework enabling efficient fusion for hypergraph neural network on gpus Z Yu, G Dai, S Yang, G Zhang, H Zhang, F Zhu, J Yang, J Zhao, Y Wang Proceedings of Machine Learning and Systems 5, 387-399, 2023 | 5 | 2023 |
Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference RB Prabhakar, H Zhang, D Wentzlaff Advances in Neural Information Processing Systems 37, 7957-7980, 2025 | 1 | 2025 |