| Accuracy vs. efficiency: Achieving both through fpga-implementation aware neural architecture search W Jiang, X Zhang, EHM Sha, L Yang, Q Zhuge, Y Shi, J Hu Proceedings of the 56th Annual Design Automation Conference 2019, 1-6, 2019 | 174 | 2019 |
| Achieving super-linear speedup across multi-fpga for real-time dnn inference W Jiang, EHM Sha, X Zhang, L Yang, Q Zhuge, Y Shi, J Hu ACM Transactions on Embedded Computing Systems (TECS) 18 (5s), 1-23, 2019 | 95 | 2019 |
| Dac-sdc low power object detection challenge for uav applications X Xu, X Zhang, B Yu, XS Hu, C Rowen, J Hu, Y Shi IEEE transactions on pattern analysis and machine intelligence 43 (2), 392-403, 2019 | 95 | 2019 |
| Algorithm-hardware co-design of attention mechanism on FPGA devices X Zhang, Y Wu, P Zhou, X Tang, J Hu ACM Transactions on Embedded Computing Systems (TECS) 20 (5s), 1-24, 2021 | 51 | 2021 |
| When neural architecture search meets hardware implementation: from hardware awareness to co-design X Zhang, W Jiang, Y Shi, J Hu 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 25-30, 2019 | 43 | 2019 |
| EF-train: Enable efficient on-device CNN training on FPGA through data reshaping for online adaptation or personalization Y Tang, X Zhang, P Zhou, J Hu ACM Transactions on Design Automation of Electronic Systems (TODAES) 27 (5 …, 2022 | 21 | 2022 |
| H2H: heterogeneous model to heterogeneous system mapping with computation and communication awareness X Zhang, C Hao, P Zhou, A Jones, J Hu Proceedings of the 59th ACM/IEEE Design Automation Conference, 601-606, 2022 | 19 | 2022 |
| Achieving full parallelism in LSTM via a unified accelerator design X Zhang, W Jiang, J Hu 2020 IEEE 38th International Conference on Computer Design (ICCD), 469-477, 2020 | 19 | 2020 |
| Xfer: A novel design to achieve super-linear performance on multiple fpgas for real-time ai W Jiang, X Zhang, EHM Sha, Q Zhuge, L Yang, Y Shi, J Hu Proceedings of the 2019 ACM/SIGDA International Symposium on Field …, 2019 | 11 | 2019 |
| Pod-racing: bulk-bitwise to floating-point compute in racetrack memory for machine learning at the edge S Ollivier, X Zhang, Y Tang, C Choudhuri, J Hu, AK Jones IEEE Micro 42 (6), 9-16, 2022 | 8 | 2022 |
| Low overhead online data flow tracking for intermittently powered non-volatile FPGAs X Zhang, C Patterson, Y Liu, C Yang, CJ Xue, J Hu ACM Journal on Emerging Technologies in Computing Systems (JETC) 16 (3), 1-20, 2020 | 6 | 2020 |
| Low overhead online checkpoint for intermittently powered non-volatile fpgas X Zhang, C Patterson, Y Liu, C Yang, CJ Xue, J Hu 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 238-244, 2018 | 5 | 2018 |
| FPIRM: Floating-point Processing in Racetrack Memories S Ollivier, X Zhang, Y Tang, C Choudhuri, J Hu, AK Jones arXiv preprint arXiv:2204.13788, 2022 | 1 | 2022 |
| Efficient Hardware and Software Design for On-device Learning Y Wu, Y Tang, D Zeng, X Zhang, P Zhou, Y Shi, J Hu Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing …, 2023 | | 2023 |
Jingtong HuUniversity of Pittsburgh, ECEVerified email at pitt.edu
