| MC-Fluid: Fluid model-based mixed-criticality scheduling on multiprocessors J Lee, KM Phan, X Gu, J Lee, A Easwaran, I Shin, I Lee 2014 IEEE Real-Time Systems Symposium, 41-52, 2014 | 87 | 2014 |
| Resource efficient isolation mechanisms in mixed-criticality scheduling X Gu, A Easwaran, KM Phan, I Shin 2015 27th Euromicro Conference on Real-Time Systems, 13-24, 2015 | 67 | 2015 |
| Dynamic budget management with service guarantees for mixed-criticality systems X Gu, A Easwaran 2016 IEEE Real-Time Systems Symposium (RTSS), 47-56, 2016 | 55 | 2016 |
| Towards safe machine learning for cps: infer uncertainty from training data X Gu, A Easwaran Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical …, 2019 | 38 | 2019 |
| Deep learning-based modeling of photonic crystal nanocavities R Li, X Gu, K Li, Y Huang, Z Li, Z Zhang Optical Materials Express 11 (7), 2122-2133, 2021 | 21 | 2021 |
| Dynamic budget management and budget reclamation for mixed-criticality systems X Gu, A Easwaran Real-Time Systems 55, 552-597, 2019 | 19 | 2019 |
| Efficient spiking neural networks with radix encoding Z Wang, X Gu, RSM Goh, JT Zhou, T Luo IEEE Transactions on Neural Networks and Learning Systems, 2022 | 18 | 2022 |
| E3NE: An end-to-end framework for accelerating spiking neural networks with emerging neural encoding on FPGAs D Gerlinghoff, Z Wang, X Gu, RSM Goh, T Luo IEEE Transactions on Parallel and Distributed Systems 33 (11), 3207-3219, 2021 | 14 | 2021 |
| Efficient schedulability test for dynamic-priority scheduling of mixed-criticality real-time systems X Gu, A Easwaran ACM Transactions on Embedded Computing Systems (TECS) 17 (1), 1-24, 2017 | 12 | 2017 |
| Smart and rapid design of nanophotonic structures by an adaptive and regularized deep neural network R Li, X Gu, Y Shen, K Li, Z Li, Z Zhang Nanomaterials 12 (8), 1372, 2022 | 9 | 2022 |
| Benchmarking quantum (-inspired) annealing hardware on practical use cases T Huang, J Xu, T Luo, X Gu, R Goh, WF Wong IEEE Transactions on Computers 72 (6), 1692-1705, 2022 | 5 | 2022 |
| The feasibility analysis of mixed-criticality systems S Ramanathan, X Gu, A Easwaran Proc. RTOPS, ECRTS 24, 2016 | 5 | 2016 |
| Hierarchical weight averaging for deep neural networks X Gu, Z Zhang, Y Jiang, T Luo, R Zhang, S Cui, Z Li IEEE Transactions on Neural Networks and Learning Systems, 2023 | 4 | 2023 |
| A resource-efficient spiking neural network accelerator supporting emerging neural encoding D Gerlinghoff, Z Wang, X Gu, RSM Goh, T Luo 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 92-95, 2022 | 4 | 2022 |
| Optimal speedup bound for 2-level mixed-criticality arbitrary deadline systems X Gu, A Easwaran Proc. RTSOPS (ECRTS), 15-16, 2014 | 4 | 2014 |
| Predicting the Q factor and modal volume of photonic crystal nanocavities via deep learning R Li, X Gu, K Li, Z Li, Z Zhang Nanophotonics and Micro/Nano Optics VII 11903, 13-24, 2021 | 3 | 2021 |
| Design and analysis for dual priority scheduling X Gu, A Easwaran, R Pathan 2018 IEEE 21st International Symposium on Real-Time Distributed Computing …, 2018 | 1 | 2018 |
| Temperature Annealing Knowledge Distillation from Averaged Teacher X Gu, Z Zhang, T Luo 2022 IEEE 42nd International Conference on Distributed Computing Systems …, 2022 | | 2022 |
| Schedulability analysis and low-criticality execution support for mixed-criticality real-time systems on uniprocessors X Gu | | 2018 |
| Self-Distillation with Model Averaging X Gu, Z Zhang, J Ran, RSM Goh, T Luo Available at SSRN 4694315, 0 | | |
