Network-on-Chip (NoC) has been proposed as a promising solution to overcome the communication challenges of System-on-Chip (SoC) design in nanoscale technologies. With the advancement in the nanoscale technology, the integration density of Intellectual Property (IP) cores in a single chip have increased, leading to heat dissipation, which in turn makes the system unreliable. Therefore, efficient fault-tolerant methods are necessary at different levels to improve overall system performance and make the system to operate normally. This article presents a flexible spare core placement technique for mesh-based NoC by taking several benchmark applications into consideration. An Integer Linear Programming (ILP)-based solution has been proposed for the spare core placement problem. Also, Particle Swarm Optimisation (PSO)-based meta-heuristic has been proposed for the same. Experiments have been performed by taking several application benchmarks reported in the literature and the applications generated using the TGFF tool. Comparisons have been carried out using our approach and the approach followed in the literature (i) by varying the network size with fixed fault percentage in the network, and (ii) by fixing the network size while varying the percentage of faults in the network. We have also compared the overall communication cost and CPU runtime between ILP and PSO approaches. The results show significant reductions in the overall communication cost, average network latency, and network power consumption across all the cases using our approach over the approaches reported in the literature.
Application mapping is one of the early stage design processes aimed to improve the performance of Network-on-Chip (NoC). Mapping is an NP-hard problem. A massive amount of high-quality supervised data is required to solve the application mapping problem using traditional neural networks. In this article, a Reinforce Learning based neural framework is proposed to learn the heuristics of the application mapping problem. The proposed reinforcement learning based mapping algorithm (RL-MAP) has actor and critic networks. The Actor is a policy network, which provides mapping sequences. The critic network estimates the communication cost of these mapping sequences. The actor network updates the policy distribution in the direction suggested by the critic. The proposed RL-MAP is trained with unsupervised data to predict the permutations of the cores to minimize the overall communication cost. Further, the solutions are improved using the 2-opt local search algorithm. The performance of RL-MAP is compared with a few well-known heuristic algorithms, Neural Mapping Algorithm (NMA) and message-passing neural network-pointer network-based genetic algorithm (MPN-GA). Results show that the communication cost and run-time of the RL-MAP improved considerably in comparison with the heuristic algorithms. The communication cost of the solutions generated by RL-MAP is nearly equal to MPN-GA and improved by 4.2% over NMA, while consuming less run time.
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