陈晖

English Version

邮箱: hui.chen@ntu.edu.sg　　　

主页：https://dawnzju.github.io/　　　　　　　　　　　　　　

GitHub：https://github.com/Dawnzju/

Linkedin: https://www.linkedin.com/in/chen-hui-33996aa6/　　

电话: (+86) 13830650260 / (+65) 87106683

研究方向

片上网络
专用集成电路通信优化
众核系统

个人经历

2022/10 - 至今　　　　　　　　　　　海思半导体有限公司　　网络架构与系统设计部

2019/01 - 2022/01　　　　　　　　　南洋理工大学　　　　　博士
导师: 刘韦辰
计算机科学与工程
绩点: 4.83/5

• 第58届dac青年院士
• 2021年ASP-DAC学生研究论坛秘书

2017/01 - 2018/07　　　　　　　　　新加坡国立大学　　　　硕士
计算学院
绩点: 4.05/5

2012/09 - 2016/06　　　　　　　　　浙江大学　　　　　　　本科
计算机科学与技术学院

• 2015 美国数学建模大赛一等奖
• 2014 辅修英语（英语文学）
• 2014 浙江大学本科生数学建模比赛二等奖
• 2013-2014 浙大新青年新媒体部门部长，负责网页和日常管理
• 2013 浙江大学新生辩论赛冠军

发表刊物

期刊

• Hui Chen, Peng Chen, Xiangzhong Luo, Shuohuai, and Weichen Liu, “LAMP: Load-balanced Multipath Parallel Transmission in Point-to-point NoCs.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). 2022.

• Hui Chen, Peng Chen, Jun Zhou, L. H. K. Duong, and Weichen Liu, “ArSMART: An Improved SMART NoC Design Supporting Arbitrary-Turn Transmission.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). 2021.

• Hui Chen, Zihao Zhang, Peng Chen, Xiangzhong Luo, Shiqing Li, and Weichen Liu, “MARCO: A High-performance Task Mapping and Routing Co-optimization Framework for Point-to-Point NoC-based Heterogeneous Computing Systems.” ACM Transactions on Embedded Computing Systems (TECS). 2021.

• Peng Chen, Hui Chen, Jun Zhou, Mengquan Li, Weichen Liu, Chunhua Xiao, Yaoyao Ye, and Nan Guan, “Contention Minimization in Emerging SMART NoC via Direct and Indirect Routes.” IEEE Transactions on Computers (TC). 2021

• Peng Chen, Weichen Liu, Hui Chen, Shiqing Li, Mengquan Li, Lei Yang, and Nan Guan, “Reduced Worst-Case Communication Latency Using Single-Cycle Multi-Hop Traversal Network-on-Chip.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). 2020.

• Shien Zhu, Luan H.K. Duong, Hui Chen, Di Liu, and Weichen Liu, “FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). 2022.

• Xiangzhong Luo, Di Liu, Shuo Huai, Hao Kong, Hui Chen and Weichen Liu, “LightNAS: On Lightweight and Scalable Neural Architecture Search for Embedded Platforms.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). 2022.

• Xiangzhong Luo, Di Liu, Shuo Huai, Hao Kong, Hui Chen, and Weichen Liu, “SurgeNAS: A Comprehensive Surgery on Hardware-Aware Differentiable Neural Architecture Search.” IEEE Transactions on Computers (TC). 2022.

• Xiangzhong Luo, Di Liu, Shuo Huai, Hao Kong, Hui Chen and Weichen Liu, “Designing Efficient DNNs via Hardware-Aware Neural Architecture Search and Beyond.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) 2021.

• Hao Kong, Shuo Huai, Di Liu, Lie Zhang, Hui Chen, Shien Zhu, Shiqing Li, Weichen Liu, Manu Rastogi, Ravi Subramaniam, and Madhu Athreya, “EDLAB: A Benchmark for Edge Deep Learning Accelerators.” IEEE Design & Test. 2021.

会议

• Hui Chen, Di Liu, Shiqing Li, Shuo Huai, Xiangzhong Luo, and Weichen Liu, “MUGNoC: A Software-configured Multicast-Unicast-Gather NoC for Accelerating CNN Dataflows.” In Proceedings of 28th Asia and South Pacific Design Automation Conference (ASP-DAC). 2023.

• Hui Chen, Zihao Zhang, Peng Chen, Xiangzhong Luo, Shiqing Lim, and Weichen Liu, “MARCO: A High-performance Task Mapping and Routing Co-optimization Framework for Point-to-Point NoC-based Heterogeneous Computing Systems.” in Proceedings of International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (ESWEEK-CASES ‘21), October 08-15, 2021, Virtual Event.

• Hui Chen, Zihao Zhang, Peng Chen, Shien Zhu, and Weichen Liu, “Parallel Multipath Transmission for Burst Traffic Optimization in Point-to-Point NoCs.” In Proceedings of the Great Lakes Symposium on VLSI 2021(GLSVLSI '21), June 22-25, 2021, Virtual Event, USA, New York, NY

• Shuo Huai, Di Liu, Xiangzhong Luo, Hui Chen, Weichen Liu, and Ravi Subramaniam, “Partial Order Based Non-Preemptive Communication Scheduling Towards Real-Time Networks-on-Chip.” In Proceedings of 28th Asia and South Pacific Design Automation Conference (ASP-DAC). 2023.

• Peng Chen, Hui Chen, Jun Zhou, Di Liu, Shiqing Li, Weichen Liu, Wanli Chang, and Nan Guan, “Partial Order Based Non-Preemptive Communication Scheduling Towards Real-Time Networks-on-Chip.” ACM Symposium on Applied Computing (SAC ‘21), March 22-26, 2021, Virtual Event.

• Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai, Hui Chen, and Weichen Liu, “You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms.” In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC ‘22). July 10-14, 2022, San Francisco, USA

专利

• 专利(Patent):
    序号: 2020-195-01-SG PRV
    题目: A High-Performance Application-Specific Network-on-Chip With Software Configurable Express Paths
    研究人员: 1) LIU Weichen; 2) CHEN Hui
    时间: 11/09/2020
    新加坡专利号: 10202008924S

• 许可(License):
    题目 : EDLAB: A Benchmark Tool for Edge Deep Learning Accelerators
    研究人员: 1) Liu Weichen; 2) Liu Di; 3) Kong Hao; 4) Zhang Lei; 5) Huai Shuo; 6) Li Shiqing; 7) Chen Hui; 8) Zhu Shien
    时间: 08/07/2020
    NTU序号: TD 2020-264

项目

• ArSMART　　　　　　　　　　　

  Url：ArSMAET

  一句话总结 : 一种支持任意路径的单周期多跳片上网络

  关键词 : 结构设计, 路由算法.

  具体贡献:

  • 我们开发了一种支持任意路径的单周期多跳片上网络，ArSMART，使用其可以大幅度减少资源冲突。具体来说，ArSMART将整个片上网络分为多个集群，在每个集群里，传输路径由控制器计算，而数据转发由无缓冲可重配置路由器进行。
  • 我们提出了相应的路由算法，使得ArSMART在正式传输之前，综合考虑网络实时状态，并且迅速地计算出路由。计算路由的难点在于网络状态在计算时和真正传输时是不同的。我们的算法减少了这种影响并且提高了片上网络性能。
     

• MARCO　　　　　　　　　　　

  Url：MARCO

  一句话总结 : 一个针对异构系统（基于ArSMART）的通信-计算联合优化框架

  关键词 : 映射算法, 路由算法，异构系统

  具体贡献:

  • 我们分析了基于片上网络的异构系统中的任务映射和通信路由的设计空间，并且发现由于任务映射和通信路由设计是强相关的，逐步地优化任务映射和通信路由无法达到最优解。
  • 我们提出了MARCO，一个针对基于片上网络的异构系统的任务映射-通信路由联合优化框架。具体来说，我们修改了tabu搜索框架来探索并且评估任务映射和通信路由计算空间。同时，我们使用增强学习来高效地找出通信路径。
     

• LAMP　　　　　　　　　　　

  Url：LAMP

  一句话总结 : 一种提高ArSMART通信并行度的方法

  关键词 : 并行多路径传输, 路由算法，网络接口设计

  具体贡献:

  • 我们修改了NoC的路由器和网络接口设计，使得其能够支持并行多路径传输，同时，在目的节点对多端口收到的包进行高效地重排。
  • 我们提出了一种并行多路径传输路由算法，这个算法很好得回答了以下三个问题：什么时候并行传输？分几条路径并行传输？每一条路径传输多少数据？以增强学习为基础，我们的算法能大幅提高传输效率。