Xiaozhe Gu
Xiaozhe Gu
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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
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
Dynamic budget management with service guarantees for mixed-criticality systems
X Gu, A Easwaran
2016 IEEE Real-Time Systems Symposium (RTSS), 47-56, 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
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
Dynamic budget management and budget reclamation for mixed-criticality systems
X Gu, A Easwaran
Real-Time Systems 55, 552-597, 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 35 (3), 3689-3701, 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
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
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
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
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
The feasibility analysis of mixed-criticality systems
S Ramanathan, X Gu, A Easwaran
Proc. RTOPS, ECRTS 24, 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
Optimal speedup bound for 2-level mixed-criticality arbitrary deadline systems
X Gu, A Easwaran
Proc. RTSOPS (ECRTS), 15-16, 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
Temperature Annealing Knowledge Distillation from Averaged Teacher
X Gu, Z Zhang, T Luo
2022 IEEE 42nd International Conference on Distributed Computing Systems …, 2022
Design and analysis for dual priority scheduling
X Gu, A Easwaran, R Pathan
2018 IEEE 21st International Symposium on Real-Time Distributed Computing …, 2018
Schedulability analysis and low-criticality execution support for mixed-criticality real-time systems on uniprocessors
X Gu
Self-Distillation with Model Averaging
X Gu, Z Zhang, J Ran, RSM Goh, T Luo
Available at SSRN 4694315, 0
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