时霄

发布者:时霄发布时间:2021-03-02浏览次数:18835


Xiao Shi(时霄)    

Ph.D., Associate Professor

School of Computer Science and Engineering, Southeast University

I am a member of PAttern Learning and Mining(PALM) Lab. 

Email: xshi@seu.edu.cn

Office: Room 146, School of Computer Science and Engineering, Southeast University Jiulonghu Campus, Nanjing, Jiangsu, China.


时霄, 博士,副教授。1990年出生于江苏南京,于东南大学吴健雄学院获得本科学位2012),美国加州大学洛杉矶分校电子工程系获得硕士学位(2016)、博士学位(2020。导师为Lei He教授。2021年起在东南大学计算机科学与工程学院任职副教授。现为东南大学PALM实验室成员。研究内容主要包括大语言模型、机器学习、电路可靠性分析等方向工作。曾承担一项美国Darpa项目。近年来,发表SCIEI论文10余篇,担任TCADTODAESTRETS等期刊审稿人。主持多项国家自然科学基金等横纵向项目。


欢迎感兴趣的本科生及研究生加入!


Selected Publications 

1. Ziqi Wang, Liang Pang, Xiao Shi, Longxing Shi. “Efficient Memory Circuits Yield Analysis and Optimization Framework via Meta-Learning”, IEEE Transactions on Circuits and Systems II: Express Briefs, (TCASII), 2024.

2. Junzhuo Zhou, Li Huang, Haoxuan Xia, Yihui Cai, Leilei Jin, Xiao Shi, Wei W. Xing, Ting-Jung Lin, Lei He. “LVF2: A Statistical Timing Model based on Gaussian Mixture for Yield Estimation and Speed Binning”, 61st ACM/IEEE Design Automation Conference (DAC), 2024.

3. Liang Pang, Ziqi Wang, Rui Shi, Mengyun Yao, Xiao Shi, Hao Yan, Longxing Shi. “An efficient SRAM yield analysis method based on scaled-sigma adaptive importance sampling with meta-model accelerated”, Integration 89 (2023): 155-167.

4. Liang Pang, Mengyun Yao, Xiao Shi, Hao Yan, Longxing Shi. “CharTM: The dynamic stability characterization for memory based on tail distribution modeling”, Microelectronics Journal 133 (2023): 105697.

5. Xiao Shi, Hao Yan, Qiancun Huang, Chengzhen Xuan, Longxing Shi, Lei He. “A Compact High-Dimensional Yield Analysis Method using Low-Rank Tensor Approximation”, Transactions on Design Automation of Electronic Systems, (TODAES), 2021. 

6. Xiao Shi, Hao Yan, Chuwen Li, Jinxin Wang, Longxing Shi, Lei He. “A Non-Gaussian Adaptive Importance Sampling Method for High-Dimensional and Multi-Failure-Region SRAM Yield Analysis”, International Conference On Computer Aided Design, (ICCAD), 2020.

7. Xiao Shi, Hao Yan, Jinxin Wang, Jiajia Zhang, Longxing Shi, Lei He. An Efficient Adaptive Importance Sampling Method for SRAM and Analog Yield Analysis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, (TCAD), 2020.

8. Peng Zou, Zhifeng Lin, Xiao Shi, Yingjie Wu, Jianli Chen, Jun Yu, and Yao-Wen Chang, Time-Division Multiplexing Based System-Level FPGA Routing for Logic Verification, 57th ACM/IEEE Design Automation Conference (DAC), 2020.

9. Shan Shen, Liang Pang, Tianxiang Shao, Ming Ling, Xiao Shi, Longxing Shi, “TYMER: A Yield based Performance Model for Timing-speculation SRAM”, 57th ACM/IEEE Design Automation Conference (DAC), 2020.

10. Xiao Shi, Hao Yan, Qiancun Huang, Jiajia Zhang, Longxing Shi, Lei He, “Meta-Model based High-Dimensional Yield Analysis using Low-Rank Tensor Approximation”, 56th ACM/IEEE Design Automation Conference, (DAC), 2019.

11. Xiao Shi, Hao Yan, Jiajia Zhang, Qiancun Huang, Longxing Shi, Lei He,Efficient Yield Analysis for SRAM and Analog Circuits using Meta-Model based Importance Sampling Method”, International Conference On Computer Aided Design, (ICCAD), 2019.

12. Xiao Shi, Hao Yan, Jinxin Wang, Xiaofen Xu, Fengyuan Liu, Longxing Shi, Lei He, “Adaptive Clustering and Sampling for High-Dimensional and Multi-Failure-Region SRAM Yield Analysis”, ACM International Symposium on Physical Design (ISPD), April 2019.

13. Xiao Shi, Jinlong Yan, Hao Yan, Jilia Zhang, Longxing Shi, Lei He,Adaptive Low-Rank Tensor Approximation for SRAM Yield Anaylsis using Bootstrp Resampling, IEEE ASIC Conference, (ASICON), 2019

14. Xiao Shi, Zhongmao Sun, Yunxuan Yu, Jun Yang, Lei He, “Low Voltage SRAM with Fault Mitigation Techniques for Energy-Efficient Convolutional Neural Networks”, in 15th Workshop on Silicon Errors in Logic – System Effects (SELSE), April 2019.

15. Xiao Shi, Fengyuan Liu, Jun Yang, Lei He, “A Fast and Robust Failure Analysis of Memory Circuits Using Adaptive Importance Sampling Method”, 55th ACM/IEEE Design Automation Conference (DAC). IEEE, 2018: 1-6.



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