/Users/jiangli/OneDrive/文档/Webpage/head.jpgLi Jiang 

Department of Computer Science & Engineering, Shanghai Jiao Tong University

Office: Rm521, SEIEE Building #03, Dong Chuan Road #800, Min Hang District, Shanghai

Tel: 86-21-34208232

Email: jiangli AT cs.sjtu.edu.cn

 

I’m recruiting Experienced FPGA engineers, Scholars in computer architecture, EDA and AI areas, and self-motivated student research assistant!

 

News

- I received the 2019 ACM Shanghai Rising Star Award!

- Our paper “System level hardware failure prediction using deep learning” is accepted by Design Automation Conference (TOP conference in EDA, CCF-A coference). This work is collaborated with Alibaba group. Congratulations to Xiaoyi Sun (1st year postgraduate)

- The 2nd ChinaDA symposium, I severed as Executive Chair, is a complete success. See report https://mp.weixin.qq.com/s/RJBl3-DvMrcivTxrH5RrMQ

- Our paper “Approximate Random Dropout for DNN Training Acceleration in GPGPU” are published in In Design, Automation & Test in Europe Conference & Exhibition, DATE 2019. “机器之心” also reported this work (see https://mp.weixin.qq.com/s/zMCBQ2D21HoDcDgDolmGMA ) Congratulations to Zhuoran Song (2nd year Ph.D student), Ru Wang, Dongyu Ru and Zhenghao Peng (4th year undergraduates).

- Our paper “HUBPA: High Utilization Bidirectional Pipeline Architecture for Neuromorphic Computing” is accepted by ACM/IEEE Asia South Pacific Design Automation Conference. Congratulations to Houxiang Ji (4th grade).

 

Background

Jiang Li is an Associate Professor in the Department of Computer Science & Engineering at Shanghai Jiaotong University. He obtained his Ph.D and MPHIL degree in the Department of Computer Science & Engineering at The Chinese University of HongKong, in 2013 and 2010 respectively. He received his B.E. degree in Computer Science & Engineering from Shanghai Jiaotong University, China in 2007.

Research

- DNN Acceleration, application-driven and accelerator-friendly DNN training

- Data mining and machine learning techniques in cloud/server system reliability/performance and chip design

- Computer Aided Design, Computer Architecture

Teaching

CS427 Multicore Architecture and Parallel Programming (2014-2016)

CS339 Computer Networks (2014-2016)

CS308 Compiler Principles

CS222 Algorithm Design and Analysis (2018-2019): The webpage will be online soon!

Honor

- Youth sailing program of excellence in science and technology, 2015

- IEEE TTTC Doctoral Thesis Award Semi-final, Asian Test Symposium, Best Thesis Award (Rank 1), Nov. 2014

- CCF-Tecent "rhino bird" creativity award fund

- Nominated for Best Research Award, The 10th ACM-HK Student Research and Career Day, Co-organized by ACM-HK and Microsoft Research Asia, November 19th, 2013

- Oversea travel grant, The 50th Design Automation Conference, 2013

- Postgraduate Studentships, The Chinese University of Hong Kong Since 2010

- Nominated for Best Paper Award, IEEE/ACM International Conference on Computer-Aided Design (ICCAD) 2010

- Certificate of Merit for Excellent Teaching Assistant Department of CS&E, CUHK, Hongkong SAR 2010

- Postgraduate Studentships, The Chinese University of Hong Kong 2008

- Outstanding graduate of colleges and universities in Shanghai, China 2007

Publication

Full Publication List

My DBLPGoogle Scholar

Recent Publication:

[1].  Xiaoyi Sun, Krishnendu Chakrabarty, Ruirui Huang, Yiquan Chen, Bing Zhao, Hai Cao, Yinhe Han, Xiaoyao Liang, Li Jiang. System level hardware failure prediction using deep learning. To appear in ACM/IEEE Design Automation Conference, Las vegas, US, 2019. (TOP conference in EDA, see http://csrankings.org)

[2].  Zhuoran Song, Ru Wang, Dongyu Ru, Zhenghao Peng, Hongru Huang, Hai Zhao, Xiaoyao Liang and Li Jiang: Approximate Random Dropout for DNN Training Acceleration in GPGPU. In Design, Automation & Test in Europe Conference & Exhibition, DATE 2019, Florence, Italy, March 21-25, 2019. (see https://mp.weixin.qq.com/s/zMCBQ2D21HoDcDgDolmGMA)

[3].  Houxiang Ji, Li Jiang, Tianjian Li, Naifeng Jing, Jing Ke, Xiaoyao Liang: HUBPA: high utilization bidirectional pipeline architecture for neuromorphic computing. Proceedings of the 24th Asia and South Pacific Design Automation Conference, ASPDAC 2019, Tokyo, Japan, January 21-24, 2019, pages 249-254.

[4].  Zhenghao Peng, Li Jiang, Xuyang Chen, Chengwen Xu, Naifeng Jing, Xiaoyao Liang and Cewu Lu. AXNet: ApproXimate computing using an end-to-end trainable neural network. In Proceedings of the International Conference on Computer-Aided Design, ICCAD 2018, San Diego, CA, USA, November 05-08, �2018, pages 11:1-11:8.

[5].  Haiyue Song, Li Jiang, Chengwen Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang and Qiang Xu. Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators. In Proceedings of the International Conference on Computer-Aided Design, ICCAD 2018, San Diego, CA, USA, November 05-08, pages 50.

[6].  Houxiang Ji, Linghao Song, Li Jiang, Hai Helen Li, and Yiran Chen. Recom: An efficient resistive accelerator for compressed deep neural networks. In 2018 Design, Automation & Test in Europe Conference & Exhibition, DATE 2018, Dresden, Germany, March 19-23, 2018, pages 237-240.

[7].  Pu Pang, Yixun Zhang, Tianjian Li, Sung Kyu Lim, Quan Chen, Xiaoyao Liang, and Li Jiang. In-growth test for monolithic 3d integrated SRAM. In Design, Automation & Test in Europe Conference & Exhibition, DATE 2018, Dresden, Germany, March 19-23, 2018, pages 569-572.

[8].  Haiyue Song, Xiang Song, Tianjian Li, Hao Dong, Naifeng Jing, Xiaoyao Liang, and Li Jiang. A FPGA friendly approximate computing framework with hybrid neural networks: (abstract only). In Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2018, Monterey, CA, USA, February 25-27, 2018, page 286, 2018.

[9].  Zhuoran Song, Dongyu Ru, Ru Wang, Hongru Huang, Zhenghao Peng, Jing Ke, Xiaoyao Liang, and Li Jiang. Approximate random dropout. CoRR, abs/1805.08939, 2018.

[10].          Zhenghao Peng, Xuyang Chen, Chengwen Xu, Naifeng Jing, Xiaoyao Liang, Cewu Lu, and Li Jiang. Axnet: Approximate computing using an end-to-end trainable neural network. CoRR, abs/1807.10458, 2018.

[11].          Li Jiang, Tianjian Li, Naifeng Jing, Nam Sung Kim, Minyi Guo, and Xiaoyao Liang. Cnfet-based high throughput SIMD architecture. IEEE Trans. on CAD of Integrated Circuits and Systems, 37(7):1331-1344, 2018.

[12].          Chen Wang, Yanan Sun, Shiyan Hu, Li Jiang, and Weikang Qian. Variation-aware global placement for improving timing-yield of carbon-nanotube field effect transistor circuit. ACM Trans. Design Autom. Electr. Syst., 23(4):44:1-44:27, 2018.

[13].          Jianfei Wang, Qin Wang, Li Jiang, Chao Li, Xiaoyao Liang, and Naifeng Jing. IBOM: an integrated and balanced on-chip memory for high performance gpgpus. IEEE Trans. Parallel Distrib. Syst., 29(3):586-599, 2018.

Finished Project

- Test Architecture Design and Optimization on three dimensional system-on-chip.

- Test and Yield Enhancement on three dimensional DRAM.

- Yield Enhancement Technique in 2.5D/3D IC.

- Board-Level Diagnosis. (Funded by Huawei)

- Computer architecture design and optimization with Carbon-Nanotube Transistors. (Funded by Shanghai Committee of Science and Technolog)

- CNN and CV algorithm acceleration with GPGPU and FPGAs (Recruiting Self Motivated Students) (Funded by AVIC)

- DNN-based object recognition in the harbor! (Industry Fund)

- DNN-aided diagnosis in medical application! (Industry Fund)

On-Going Project

- NEW! Neural-network and accelerator architecture search. (Funed by SENSETIME), PI, 2019-2020

- NEW! Performance and Reliability Enhancement in server clusters using Machine learning. (Funded by Alibaba), PI, 2019-2020

- NEW! High efficient and reliable ReRAM-based Accelerator for deep learning. (Funded by HUAWEI), PI, 2019-2020

- NEW! DNN Training� framework in the mobile device. (Funded by HUAWEI), PI, 2019-2020

- NEW! Neuro Processing Unit for approximate computing: architecture and applications! (Funded by NSFC major project), co-PI, 2019-2021

- NEW! Crowed Test platform for ICT. (funded by 国家重点研发计划), co-PI, 2019-2021

- NEW! Online trainable Neuromorphic architecture. (Funded by Shanghai Science and Technology Console.) , PI, 2017-2019

- NEW! Reliable 3D monolithic Integrated CNFET SRAMs! (Funded by NSFC youth fund), PI, 2017-2019

Research Group

Current:

PhD: Fangxin Liu, Zhuoran Song(co-supervised);

Master: Zishan Jiang (co-supervised); Chengwen Xu(co-supervised); Xiaoyi Sun, Xingyi Wang, Yilong Zhao, Chaoqun Chu

Undergraduate (Senior): Hongyu Chen, Fuyuan Lv, Shenghan Yu, Xiaoqiao Xu, Yuqiang Xiao

Undergraduate (Junior): Ruofan Liang, Dongyue Li, Mengqi Cao, Chenyu Yang

Alumni:

Graduated in 2018: Jun Li (miHoYo); Hao Dong (A finance company -_-!); Yi Liu (DJI); Lerong Chen (Entrepreneurship); Tianjian Li (First Position Sensetime)

Graduated in 2017: Feng Xie (First Position Huawei), Xiangyu Wu (First Position: Google), Xiangwei Huang (First Position: Huawei)

Graduated in 2016: Yihuang Huang (Netease Games), Hao Chen (UT-Austin), Mengyun Liu (Duke), Wenkang Yu (UCSD), Jiawen Li (UCLA), Xiangyu Bi (UT-Austin), Yan Han, Chengkai Zhu (UCSD)

 

Research Activity

Chair: chair in ChinaDA, Tutorial Chair in ITC-Asia, Workshop Chair in CTC/CFTC

Associate Editor: IET Journal on Computers & Digital Techniques

TPC Member: Design Automation and Test in Europe Conference (DATE); Asia and South Pacific Design Automation Conference (ASP-DAC); Asian Test Symposium (ATS); 3D-Test workshop; IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), IEEE Computer Society Annual Symposium on VLSI (ISVLSI),

Reviewer: IEEE Transaction on CAD of Integrated Circuits and Systems (TCAD), IEEE Transactions on Very Large Scale Integration (VLSI) Systems (TVLSI), IEEE Transactions on Computer (TC), ACM/IEEE Design Automation Conference (DAC), Asian Test Symposium conference (ATS).

Sponsors

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