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

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.