首页 | 本学科首页   官方微博 | 高级检索  
     


Bi-LCQ: A low-weight clustering-based Q-learning approach for NoCs
Authors:F Farahnakian  M Ebrahimi  M Daneshtalab  P Liljeberg  J Plosila
Affiliation:Department of Information Technology, University of Turku, Turku, Finland
Abstract:Network congestion has a negative impact on the performance of on-chip networks due to the increased packet latency. Many congestion-aware routing algorithms have been developed to alleviate traffic congestion over the network. In this paper, we propose a congestion-aware routing algorithm based on the Q-learning approach for avoiding congested areas in the network. By using the learning method, local and global congestion information of the network is provided for each switch. This information can be dynamically updated, when a switch receives a packet. However, Q-learning approach suffers from high area overhead in NoCs due to the need for a large routing table in each switch. In order to reduce the area overhead, we also present a clustering approach that decreases the number of routing tables by the factor of 4. Results show that the proposed approach achieves a significant performance improvement over the traditional Q-learning, C-routing, DBAR and Dynamic XY algorithms.
Keywords:Network-on-Chip  Congestion-aware routing algorithm  Adaptive routing algorithm  Q-learning and Q-routing approaches
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号