Chen Hui

中文简历

Email: hui.chen@ntu.edu.sg　　　

blog：https://dawnzju.github.io/　　　　　　　　　　　　　　　

GitHub：https://github.com/Dawnzju/　

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

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

Research Interests

Electronic/Photonic Network-on-Chip
Application-specific Designs
Many-Core Systems

Education

2019/01 - Now　　　　　　　 　　Nanyang Technological University　　　PhD Candidate
Supervisor: Liu Weichen
School of Computer Science and Engineering
Grade: 4.83/5

• Young Fellow of the 58th Design Automation Conference
• Secretary of ACM SIGDA Student Research Forum (co-located with ASP-DAC 2021)

2017/01 - 2018/07　　　　　　　　　National University of Singapore　　　Master
School of Computing
Grade: 4.05/5

2012/09 - 2016/06　　　　　　　　　Zhejiang University　　　　　　　　　Undergraduate
School of Computer Science and Technology

• 2015 1st grade award of The Mathematical Contest in Modeling of America.
• 2014 Minor in English, focus on English literature.
• 2014 2nd grade award of The Undergraduate Mathematical Contest in Modeling of Zhejiang University.
• 2013-2014 Minister of The New Youth Network Studio, responsible for the website and daily operations.
• 2013 The Best Debater of The new Undergraduate debate competition of Zhejiang University.

Publication

Journal

• Hui Chen, Peng Chen, Xiangzhong Luo, Shuohuai, 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, 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.

• Weigang Hou, Pengxing Guo, Lei Guo, Xu Zhang, Hui Chen, Weichen Liu. “O-Star: An Optical Switching Architecture Featuring Mode and Wavelength-Division Multiplexing for On-chip Many-Core Systems.” Journal of Lightwave Technology (JLT). 2021.

• 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, Madhu Athreya, “EDLAB: A Benchmark for Edge Deep Learning Accelerators.” IEEE Design & Test. 2021.

Conference

• 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.” 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, 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

• 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

Patents

• Technology Disclosure (TD):
    Ref: 2020-195-01-SG PRV
    Title: A High-Performance Application-Specific Network-on-Chip With Software Configurable Express Paths
    Inventors: 1) LIU Weichen; 2) CHEN Hui
    Filed date: 11 September 2020
    Singapore provisional patent application number: 10202008924S

• License:
    Title : EDLAB: A Benchmark Tool for Edge Deep Learning Accelerators
    Inventors: 1) Liu Weichen; 2) Liu Di; 3) Kong Hao; 4) Zhang Lei; 5) Huai Shuo; 6) Li Shiqing; 7) Chen Hui; 8) Zhu Shien
    Filed date: 08 July 2020
    NTU Ref: TD 2020-264

Main Projects

• ArSMART　　　　　　　　　　　

  Url：ArSMAET

  One sentence for contribution : A NoC infrastructure that supports single-cycle-multi-hop transmission with arbitrary turns.

  Related to : NoC infrastructure design, routing design.

  Specifically:

  • We develop an NoC design, ArSMART NoC, to set up single-cycle long-distance paths and support arbitrary-turn data transmission, which significantly reduces resource contentions. Specifically, ArSMART divides the whole NoC into multiple clusters where the route computation is conducted by the cluster controller and the data forwarding is performed by the bufferless reconfigurable router.
  • We present corresponding routing algorithms that enable ArSMART to manage NoC resources efficiently. Specifically, we conduct the route computation to generate a route before they demand at runtime, considering the real-time network state. The challenge to design routing algorithms for ArSMART is the difference of network states used in route computation and actual transmission. Our algorithms manage to minimize such impact and lessen contentions to improve NoC performance.
  • We implement the ArSMART design and matched routing algorithms in Gem5, and conduct a full system simulation to show their effectiveness. Compared with the state-of-the-art SMART NoC, the experimental results demonstrate an average reduction of 40.7% in application schedule length and 29.7% in energy consumption.

• MARCO　　　　　　　　　　　

  Url：MARCO

  One sentence for contribution : A computation and communication optimization framework for heterogeneous many-cores (based on ArSMART).

  Related to : Mapping, Routing, Heterogeneous systems.

  Specifically:

  • We analyze the design space of task mapping and routing for emerging NoC-based HCSs. We identify that algorithms that unilaterally explore task mapping or routing cannot get the optimal solution since task mapping and routing are strongly related.
  • We propose MARCO, a task mapping and routing co-optimization framework for emerging NoC-based HCSs, to decrease the schedule length of applications. Specifically, we revise the tabu search to explore the design space and evaluate the quality of task mapping and routing. The advanced reinforcement learning algorithm, i.e., advantage actor-critic, is adopted to compute paths efficiently.
  • We perform extensive experiments on various real applications, which demonstrates that the MARCO achieves a remarkable performance improvement in terms of schedule length (+44.94% ~ +50.18%) when compared with the state-of-the-art mapping and routing co-optimization algorithm for homogeneous computing systems. We also compare MARCO with different combinations of state-of-the-art independent mapping and routing approaches.

• LAMP　　　　　　　　　　　

  Url：LAMP

  One sentence for contribution : A methodology to improve data transmission parallelism for ArSMART.

  Related to : Parallel multipath transmission, Routing, NI design.

  Specifically:

  • We revised NoC router and network interface (NI) designs to support the parallel multipath transmission competently and re-order the packets from different ports with minimal overhead.
  • We proposed a parallel multipath algorithm, with which, the complex problems: when to split data transmission, how to split it, and which path should be taken to transmit data, are efficiently answered through the reinforcement learning-based approach to improve the NoC performance.
  • We presented temporal and spatial load balancing algorithms to adjust the size of split messages so that the NoC resources can be fully utilized, further improving the transmission efficiency.