蒋力2007年于上海交通大学获得计算机科学与技术学士学位，2013年于香港中文大学计算机科学与工程系获得博士学位。2013年赴美国杜克大学ECE系访问学者。从事芯片设计，设计自动化（EDA），计算机体系结构，基于机器学习算法的芯片及硬件系统性能、可靠性提升等。
从事芯片的，基于机器学习算法的芯片及硬件系统性能、可靠性提升等。在计算机体系结构及芯片设计自动化领域的主流国际会议和期刊，如ISCA、MICRO、DAC、ICCAD、DATE、TCAD、TVLSI等上发表论文近60篇，其中一篇获ICCAD最佳论文提名。据IEEE数字图书馆统计，5篇论文在其会议所收录的所有论文中引用数排前5%（分别为4/330，5/122，3/143，4/328，6/131）。他在MICRO, DATE, ASP-DAC, ITC-Asia, ATS, CFTC, CTC等国际和国家会议中担任联席主席和TPC成员。他是IET Computers & Digital Techniques，Integration the VLSI Journal等国际知名集成电路杂志编委。同时，他还是高等教育出版社人工智能实践系列课程与教材编委会委员。
成果得到业界专家的引用和高度评价，包括中国科学院郑南宁院士，美国工程院William Dally 院士，台积电前CTO Chengming Hu，及多名 ACM/IEEE fellow。3D IC测试架构方面的工作引用达数百次，已被纳入IEEE P1838标准；3D存储器容错架构中的资源共享技术获得上百次引用不仅获得学术界主流认可，而且在台积电落地并联合发表论文；多项技术在业界通过华为、阿里巴巴等企业等产品线测试，及大规模部署试用。
2019年8月，CCF（中国计算机学会）集成电路Early Career Award；
2019年6月，中国图灵大会，ACM上海新星奖；
2015年10月，国际测试会议（ITC），IEEE芯片测试技术委员会E. J. McCluskey Doctoral Thesis Award，亚洲区决赛第一名，总决赛入围奖；
2014年11月，亚洲测试会议（ATS），最佳博士论文奖。
1. Yu Gong, Zhihan Xu, Zhezhi He, Weifeng Zhang, Xiaobing Tu, Xiaoyao Liang, Li Jiang，N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores，Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays(FPGA), 2022
2. Fangxin Liu，Haomin Li，Xiaokang Yang，Li Jiang, L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks, 2022 International Conference on Research and Development in Information Retrieva(SIGIR), 2022
3. Xuan Zhang, Zhuoran Song, Xing Li, Linan Yang, Qijun Zhang, Zhezhi He, Li Jiang, Naifeng Jing and Xiaoyao Liang, IHAA: An Item-Hotness-Aware RRAM-based Accelerator for Recommendation Model, 2022 IEEE International Conference on Computer Design(ICCD), 2022
4. Fangxin Liu, Zongwu Wang, and Li Jiang, Irregular and Match: A Co-Design Framework for Energy Efficient Processing in Spiking Neural Networks, 2022 IEEE International Conference on Computer Design(ICCD), 2022
5. Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yilong Zhao, Tao Yang, Yiran Chen and Li Jiang, IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization, 2022 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD), 2022
6. Weidong Cao, Yilong Zhao, (CO-first author), Boloor Adith Jagadish, Yinhe Han, Xuan Zhang, Li Jiang, Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals, 2022 IEEE Transactions on Computers(TC), 2022
7. Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Tao Yang, Zhezhi He, Xiaokang Yang and Li Jiang, SATO: Spiking Neural Network Acceleration via Temporal-Oriented Dataflow and Architecture, 2022 ACM/IEEE Design Automation Conference(DAC), 2022
8. Qidong Tang, Zhezhi He, Fangxin Liu, Zongwu Wang, Yiyuan Zhou, Yinghuan Zhang, Li Jiang, HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine, 2022 27th Asia and South Pacific Design Automation Conference(ASP-DAC), 2022
9. Zhi Li, Yanan Sun, Zhezhi He, Liukai Xu, Li Jiang, CIM-ISP: Computing In-Memory for Image Signal Processing, 2022 27th Asia and South Pacific Design Automation Conference(ASP-DAC), 2022
10. Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Tao Yang and Li Jiang, SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training, Frontiers in Neuroscience, section Neuromorphic Engineering, 2022
11. Fangxin Liu,Zongwu Wang,Yongbiao Chen, Zhezhi He, Tao Yang, Xiaoyao Liang, and Li Jiang, SoBS-X:Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator, 2022 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD), 2022
12. Zongwu Wang,Zhezhi He, Rui Yang,Shiquan Fan，Jie Lin, Fangxin Liu，Yueyang Jia, Chenxi Yuan，Qidong Tang and Li Jiang, Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing, 2022 Design, Automation & Test in Europe Conference & Exhibition(DATE), 2022
13. Tao Yang，Dongyue Li，Fei Ma，Zhuoran Song，Yilong Zhao，Jiaxi Zhang，Fangxin Liu and Li Jiang, PASGCN: An ReRAM-Based PIM Design for GCN with Adaptively Sparsified Graphs, 2022 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD), 2022
14. Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He and Li Jiang, DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture, 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022
15. Fangxin Liu，Wenbo Zhao, Zongwu Wang,Yongbiao Chen,Li Jiang, SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks, 2022 AAAI Conference on Artificial Intelligence(AAAI), 2022
16. Fangxin Liu, Wenbo Zhao,Yongbiao Chen,Zongwu Wang,Zhezhi He,Rui Yang,Qidong Tang, Tao Yang,Cheng Zhuo and Li Jiang, PIM-DH: ReRAM-based Processing-in-Memory Architecture for Deep Hashing Acceleration, 2022 ACM/IEEE Design Automation Conference(DAC), 2022
17. Fangxin Liu, Wenbo Zhao, Zongwu Wang,Qidong Tang, Yongbiao Chen,Zhezhi He,Naifeng Jing,Xiaoyang Liang and Li Jiang, EBSP: Evolving Bit Sparsity Patterns for Hardware-Friendly Inference of Quantized Deep Neural Networks, 2022 ACM/IEEE Design Automation Conference(DAC), 2022
18. Yunyan Hong, Qiang Xu and Li Jiang，Skimming and Scanning for Untrimmed Video Action Recognition, 2021 International Congress on Image and Signal Processing, BioMedical Engineering and Informatics(CISP-BMEI), 2021
19. Feiyang Wu, Zhuoran Song, Jing Ke, Li Jiang, Naifeng Jing and Xiaoyao Liang, IPU: Domain Specific Programmable Parallel Microarchitecture for Image Processing, 2021 International Symposium on Parallel and Distributed Processing with Applications(ISPA) , 2021
20. Ziqi Meng, Weikang Qian, Yanan Sun, Yilong Zhao, Rui Yang, and Li Jiang, Digital offset for RRAM-based neuromorphic computing: a novel solution to conquer cycle-to-cycle variation, Proceedings of the 2021 Design, Automation, and Test in Europe Conference(DATE), 2021
21. Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Tao Yang, Xiaoyao Liang, Naifeng Jing and Li Jiang, SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network, 2021 International Conference on Computer Design (ICCD), 2021
22. Zhuoran Song, Yanan Sun, Lerong Chen, Tianjian Li, Naifeng Jing, Xiaoyao Liang, Li Jiang, ITT-RNA: Imperfection Tolerable Training for RRAM-Crossbar-Based Deep Neural-Network Accelerator, 2021 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD), 2021
23. Tao Yang, Zhezhi He, Tengchuan Kou, Qingzheng Li, Qi Han, Haibao Yu, Fangxin Liu, Yun Liang, and Li Jiang,BISWSRBS: A Winograd-based CNN Accelerator with a Fine-grained Regular Sparsity Pattern and Mixed Precision Quantization, ACM Trans. Reconfigurable Technol, 2021
24. Zhuoran Song, Dongyue Li, Zhezhi He, Xiaoyao Liang, Li Jiang, ReRAM-Sharing: Fine-Grained Weight Sharing for ReRAM-Based Deep Neural Network Accelerator, 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021
25. Fangxin Liu, Wenbo Zhao, Zongwu Wang, Tao Yang, Li Jiang, IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration, 2021 ACM Great Lakes Symposium on VLSI(GLSVLSI), 2021
26. Hanchen Guo, Zhehan Lin, Yunfei Gu, Chentao Wu, Li Jiang, Jie Li, Guangtao Xue, Minyi Guo, Lazy-WL: A Wear-aware Load Balanced Data Redistribution Method for Efficient SSD Array Scaling, 2021 IEEE International Conference on Cluster Computing (CLUSTER), 2021
27. Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Tao Yang and Li Jiang, SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training, Frontiers in Neuroscience, 2021
28. Dongyue Li,Tao Yang,Lun Du,Zhezhi He,Li Jiang, AdaptiveGCN: Efficient GCN Through Adaptively Sparsifying Graphs, 2021 International Conference on Information and Knowledge Management(CIKM), 2021
29. Fangxin Liu,Wenbo Zhao,Zhezhi He,Yanzhi Wang,Zongwu Wang, Changzhi Dai, Xiaoyao Liang, Li Jiang, Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point, 2021 International Conference on Computer Vision(ICCV), 2021
30. Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Yongbiao Chen, Li Jiang, Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator, 2021 International Conference on Computer-Aided Design (ICCAD), 2021
31. Xingyi Wang, Yu Li, Yiquan Chen, Shiwen Wang, Yin Du, Cheng He, YuZhong Zhang, Pinan Chen, Xin Li, Wenjun Song, Qiang xu, Li Jiang, On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers, 2021 IEEE VLSI Test Symposium(VTS), 2021
32. Zhuoran Song, Dongyue Li, Zhezhi He, Xiaoyao Liang, Li Jiang, ReRAM-Sharing: Fine-Grained Weight Sharing for ReRAM-Based Deep Neural Network Accelerator, 2021 International Symposium on Circuits and Systems (ISCAS), 2021
33. Fangxin Liu, Wenbo Zhao, Zongwu Wang, Tao Yang, Li Jiang, IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration, 2021 ACM Great Lakes Symposium on VLSI(GLSVLSI), 2021
34. Yilong Zhao, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang, Re2PIM: A Reconfigurable ReRAM-based PIM Design for Variable-sized Vector-Matrix Multiplication,2021 ACM Great Lakes Symposium on VLSI (GLSVLSI), 2021
35. Tao Yang, Dongyue Li, Yibo Han, Yilong Zhao, Fangxin Liu, Xiaoyao Liang, Zhezhi He, Li Jiang, PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration, 2021 ACM/IEEE Design Automation Conference(DAC), 2021
36. Yanan Sun, Chang Ma, Zhi Li, Yilong Zhao, Jiachen Jiang, Weikang Qian, Rui Yang, Zhezhi He,Unary Coding and Variation-Aware Optimal Mapping Scheme for Reliable ReRAM-based Neuromorphic Computing, 2021 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD), 2021
