Jingweijia Tan, Ph.D.

Associate Professor

College of Computer Science and Technology
Jilin University
2699 Qianjin Street,
Changchun, Jilin, China, 130012
Email: jtan AT jlu.edu.cn

About me
I received my Ph.D. degree in Electrical Engineering from University of Houston, M.S. degree in Computer Science from University of Kansas, and B.S. degree in Computer Science and Technology from Jilin University. I interned twice in Pacific Northwest National Laboratory during my PhD. Before joining Jilin University, I worked at Qualcomm Inc. as a Senior Engineer.

 

Research Interests
My research areas are computer architecture and high-performance computing (HPC), focusing on building reliable and energy-efficient computer systems from multiple disciplines (e.g., architecture, compiler, and application). Please check more about my research at ETECA Lab website.

 

Publications

Book Chapters
  1. Jingweijia Tan, and Xin Fu, Addressing Hardware Reliability Challenges in General-Purpose GPUs, in Advances in GPU Research and Practice, Elsevier Publishing, 2016.
Journals
  1. Jingweijia Tan, Liqi Ping, Qixiang Wang, Kaige Yan, Saca-AVF: A Quantitative Approach to Analyze the Architectural Vulnerability Factors of CNN Accelerators, IEEE Transactions on Computers (TC), 2023.
  2. Jingweijia Tan, Keyu Chen, Weiren Wang, Kaige Yan, Xiaohui Wei, MCM-GPU Voltage Noise Characterization and Architecture-Level Mitigation, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023.
  3. Jingweijia Tan, Weiren Wang, Maodi Ma, Xiaohui Wei, Kaige Yan, Improving the Performance of CNN Accelerator Architecture Under the Impact of Process Variations, ACM Transactions on Design Automation of Electronic Systems (TODAES), 2023.
  4. Jingweijia Tan, Qixiang Wang, Kaige Yan, Xiaohui Wei, Saca-FI: A microarchitecture-level fault injection framework for reliability analysis of systolic array based CNN accelerator, Future Generation Computer Systems (FGCS), 2023. [CODE]
  5. Tiancong Bu, Kaige Yan, Jingweijia Tan, Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs, ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022.
  6. Hengshan Yue, Xiaohui Wei, Jingweijia Tan, Nan Jiang, Meikang Qiu, Eff-ECC: Protecting GPGPUs Register File with a Unified Energy-Efficient ECC Mechanism, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) 2021.
  7. Kaige Yan, Jingweijia Tan, Longjun Liu, Xingyao Zhang, Stanko R. Brankovic, Jinghong Chen, Xin Fu, Toward Customized Hybrid Fuel-Cell and Battery-powered Mobile Device for Individual Users, ACM Transactions on Embedded Computing Systems (TECS) 2020.
  8. Jingweijia Tan, Kaige Yan, Shuaiwen Leon Song, Xin Fu, Energy-Efficient GPU L2 Cache Design Using Instruction-Level Data Locality Similarity, ACM Transactions on Design Automation of Electronic Systems (TODAES), 2020.
  9. Kaige Yan, Jingweijia Tan, Xin Fu, Improving Energy Efficiency of Mobile Devices by Characterizing and Exploring User Behaviors, Journal of System Architecture (JSA) 2019.
  10. Kaige Yan, Jingweijia Tan, Xin Fu, Bridging mobile device configuration to the user experience under budget constraint, Pervasive and Mobile Computing (PMC), Volume 58, August 2019.
  11. Jingweijia Tan and Kaige Yan, Efficiently Managing the Impact of Hardware Variability on GPUs' Streaming Processors, ACM Transactions on Design Automation of Electronic Systems (TODAES), 24(1), January 2019.
  12. Chenhao Xie, Jingweijia Tan, Mingsong Chen, Yang Yi, Lu Peng, and Xin Fu, Emerging Technology Enabled Energy-Efficient GPGPUs Register File, Elsevier Journal of Microprocessors and Microsystems (MICPRO), May 2017.
  13. Jingweijia Tan, Mingsong Chen, Yang Yi, and Xin Fu, Mitigating the Impact of Hardware Variability for GPGPUs Register File, IEEE Transactions on Parallel and Distributed Systems (TPDS), 27(11), November 2016.
  14. Jingweijia Tan, Zhi Li, Mingsong Chen, and Xin Fu, Exploring Soft-Error Robust and Energy-Efficient Register File in GPGPUs using Resistive Memory, ACM Transactions on Design Automation of Electronic Systems (TODAES), 21(2), January 2016.
  15. Jingweijia Tan, Yang Yi, Fangyang Shen, and Xin Fu, Modeling and Characterizing GPGPU Reliability in the Presence of Soft Errors, Elsevier Journal of Parallel Computing (ParCo), 39(9), November 2013.
Conferences
  1. Jingweijia Tan, Keyu Chen, Kaige Yan, MG-Voltage: Characterizing and Mitigating Voltage Noise in MCM-GPU Architectures, International Conference on Computer Design (ICCD), October 2022.
  2. Hengshan Yue, Xiaohui Wei, Guangli Li, Jianpeng Zhao, Nan Jiang, Jingweijia Tan, G-SEPM: Building an Accurate and Efficient Soft Error Prediction Model for GPGPUs, International Conference for High Performance Computing, Networking, Storage and Analysis (SC), November 2021.
  3. Ben Li, Jingweijia Tan, Kaige Yan, AERO: Towards Energy-Efficient Autonomous Flight in MAVs Using Approximate Execution, International Conference on Application-specific Systems, Architectures and Processors (ASAP), July 2021.
  4. Xiaohui Wei, Hengshan Yue, Jingweijia Tan, LAD-ECC: Energy-Efficient ECC Mechanism for GPGPUs Register File, Design, Automation and Test in Europe Conference & Exhibition (DATE), March 2020.
  5. Liqi Ping, Jingweijia Tan, Kaige Yan, SERN: Modeling and Analyzing the Soft Error Reliability of Convolutional Neural Networks, ACM Great Lakes Symposium on VLSI (GLSVLSI), Septemper 2020 (poster).
  6. Maodi Ma, Jingweijia Tan, Xiaohui Wei, Kaige Yan, Process Variation Mitigation on Convolutional Neural Network Accelerator Architecture, International Conference on Computer Design (ICCD), November 2019.
  7. Jingweijia Tan, Kaige Yan, Shuaiwen Leon Song, and Xin Fu, LoSCache: Leveraging Locality Similarity to Build Energy-Efficient GPU L2 Cache, Design, Automation and Test in Europe Conference & Exhibition (DATE), March 2019.
  8. Jingweijia Tan and Kaige Yan, HVSM: Hardware-Variability Aware Streaming Processors' Management Policy in GPUs, Design, Automation and Test in Europe Conference & Exhibition (DATE), March 2018. (Best Paper Nominee)
  9. Kaige Yan, Xingyao Zhang, Jingweijia Tan, and Xin Fu, Redefining QoS and Customizing the Power Management Policy to Satisfy Individual Mobile Users, International Symposium on Microarchitecture (MICRO), October 2016.
  10. Jingweijia Tan, Shuaiwen Leon Song, Kaige Yan, Xin Fu, Andres Marquez, and Darren Kerbyson, Combating the Reliability Challenge of GPU Register File at Low Supply Voltage, International Conference on Parallel Architectures and Compilation Techniques (PACT), September 2016.
  11. Jingweijia Tan and Xin Fu, Mitigating the Susceptibility of GPGPUs Register File to Process Variations, IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2015.
  12. Jingweijia Tan, Zhi Li, and Xin Fu, Soft-Error Reliability and Power Co-Optimization for GPGPU Register File using Resistive Memory, Design, Automation and Test in Europe Conference & Exhibition (DATE), March 2015.
  13. Jingweijia Tan, Zhi Li, and Xin Fu, Cost-Effective Soft-Error Protection for SRAM-Based Structures in GPGPUs, ACM International Conference on Computing Frontiers (CF), May 2013.
  14. Zhi Li, Jingweijia Tan, and Xin Fu, Hybrid CMOS-TFET based Register File for Energy-Efficient GPGPUs, International Symposium on Quality Electronic Design (ISQED), March 2013.
  15. Jingweijia Tan and Xin Fu, RISE: Improving Streaming Processors Reliability Against Soft Errors in GPGPUs, International Conference on Parallel Architectures and Compilation Techniques (PACT), September 2012.
  16. Jingweijia Tan, Nilanjan Goswami, Tao Li, and Xin Fu, Analyzing Soft-Error Vulnerability on GPGPU Microarchitecture, International Symposium on Workload Characterization (IISWC), November 2011.

 

Grants
National Natural Science Foundation of China, 2024-2027, PI.
Jilin Scientific and Technological Development Program, 2023-2025, PI.
Jilin Scientific and Technological Development Program, 2022-2025, PI.
Jilin Scientific and Technological Development Program, 2019-2021, PI.
National Natural Science Foundation of China, 2019-2021, PI.
Jilin Scientific and Technological Development Program, 2018-2020, PI.

 

Teaching
Fall 2023, GPGPU Heterogeneous and High Performance Computing
Fall 2023, Computer Hardware System Design Experiments
Spring 2023, Computer Architecture
Fall 2022, GPGPU Heterogeneous and High Performance Computing
Spring 2022, Cloud and Distributed Computing Technology
Fall 2021, GPGPU Heterogeneous and High Performance Computing
Spring 2021, Computer Architecture
Spring 2020, Cloud Computing Technology
Spring 2019, Cloud Computing Technology
Spring 2019, Computer Architecture
Spring 2018, Cloud Computing Technology

 

For Prospective Students
Basic requirements: be familiar with Linux, C/C++ programming, and script languages (e.g., Python); have taken several of the following undergraduate-level courses: computer architecture, computer system organization, compilers, assembly language, and operating systems.

About computer architecture research: computer architecture is the art of designing and implementing computer systems, so as to meet the requirements of performance, functionality, power, energy, reliability, cost, and so on. Here are several popular research topics: memory-hierarchy design, parallelism, network-on-chip, scheduling, and specialized processor design (e.g., machine learning and Internet of things). If you would like to know the recent advances in this field, you can check the top international conferences like ISCA, MICRO, and HPCA. These conferences are highly competitive with usually about 20% acceptance rate.

Academic tips: advice collection by Professor Tao Xie.