A reconfigurable fault-tolerant deflection routing algorithm based on reinforcement learning for network-on-chip

  • Authors:

  • Chaochao Feng;Zhonghai Lu;Axel Jantsch;Jinwen Li;Minxuan Zhang

  • Affiliations:

  • National University of Defense Technology, Changsha, P.R. China and Royal Institute of Technology, Stockholm, Sweden;Royal Institute of Technology, Stockholm, Sweden;Royal Institute of Technology, Stockholm, Sweden;National University of Defense Technology, Changsha, P.R. China;National University of Defense Technology, Changsha, P.R. China

  • Venue:
  • Proceedings of the Third International Workshop on Network on Chip Architectures
  • Year:
  • 2010

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Abstract

We propose a reconfigurable fault-tolerant deflection routing algorithm (FTDR) based on reinforcement learning for NoC. The algorithm reconfigures the routing table through a kind of reinforcement learning---Q-learning using 2-hop fault information. It is topology-agnostic and insensitive to the shape of the fault region. In order to reduce the routing table size, we also propose a hierarchical Q-learning based deflection routing algorithm (FTDR-H) with area reduction up to 27% for a switch in an 8 x 8 mesh compared to the original FTDR. Experimental results show that in the presence of faults, FTDR and FTDR-H are better than other fault-tolerant deflection routing algorithms and a turn model based fault-tolerant routing algorithm.