刘大江，博士，重庆大学计算机学院副教授、硕士生导师；2009年本科毕业于电子科技大学微电子技术专业；同年9月进入清华大学微纳电子系直接攻读博士学位，2015年获得工学博士学位；同年8月进入清华大学计算机系进行博士后研究，2017年出站；同年9月进入重庆大学计算机学院工作至今，现为重庆大学计算机学院副教授，于2018年在澳大利亚昆士兰大学和悉尼科技大学进行访问交流。研究领域包括可重构计算架构及编译优化、图计算硬件加速器和三维场景表示加速器；曾主持和参与了国家自然科学基金、科技创新2030-新一代人工智能、华为高校合作技术合作和CCF-腾讯犀牛鸟基金等多个项目；在DAC、ICCAD、DATE、TCAD、TVLSI等重要国际会议和期刊发表十余篇论文；现为IEEE和中国计算机协会会员；曾获教育部科技成果完成者证书。

部分论文：

1. D. Liu*, Y. Xia, J. Shang, J. Zhong, S. Yin. E2EMap: End-to-End Reinforcement Learning for CGRA Compilation via Reverse Mapping, IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2024, pp.46-60. (CCF-A)

2. D. Liu*, D. Pan, X. Xiong, J. Shang, S. Yin. PMP: Pattern Morphing-based Memory Partitioning in High-Level Synthesis, ACM/EDAC/IEEE Design Automation Conference (DAC), 2024, pp. 1-6. (CCF-A)

3. Y. Liang, D. Mou, D. Liu*, DISC: Exploiting Data Parallelism of Non-Stencil Computations on CGRAs via Dynamic Iteration Scheduling, 2024 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2024, pp. 1-9. (CCF-B, To appear.)

4. X. Mo, Y. Li, D. Liu*, Optimizing Imperfectly-Nested Loop Mapping on CGRAs via Polyhedral-Guided Flattening, 2024 Design, Automation and Test in Europe Conference (DATE), 2024, pp. 1-6. (CCF-B)

5. D. Liu*, D. Mou，R. Zhu，J. Shang，J. Zhong, S. Yin*. DARIC: A Data Reuse-Friendly CGRA for Parallel Data Access via Elastic FIFOs, ACM/EDAC/IEEE Design Automation Conference (DAC), 2023, pp. 1-6.  (CCF-A)

6. L. Huang，D. Liu*. Optimizing Data Reuse for CGRA Mapping Using Polyhedral-based Loop Transformations, ACM/EDAC/IEEE Design Automation Conference (DAC), 2023, pp. 1-6. (CCF-A)

7. D. Liu*, S. Yin, G. Luo, J. Shang, L. Liu, S. Wei, Y. Feng, S. Zhou. Data-Flow Graph Mapping Optimization for CGRA with Deep Reinforcement Learning, IEEE Trans. on Computer-Aided Design (TCAD) of Integrated Circuits and Systems,2019,38(12):2271-2283. (CCF-A)

8. Y. Zhuang, Z. Zhang, D. Liu*. Towards High-Quality CGRA Mapping with Graph Neural Networks and Reinforcement Learning, 2022 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2022, pp. 1-9. (CCF-B)

9. D. Liu*, T. Liu, X. Mo, J. Shang, J. Zhong, S. Yin. Polyhedral-based Pipelining of Imperfectly-Nested Loop for CGRAs, 2021 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2021, pp. 1-9. (CCF-B)

10.  B. Wang, R. Zhu, J. Shang, D. Liu*, Towards Energy-Efficient CGRAs via Stochastic Computing[C], 2022 Design, Automation and Test in Europe Conference (DATE), 2022, pp. 1-6. (CCF-B)