Fault-Tolerant Routing Algorithm for Mesh based NoC using Reinforcement Learning

Samala, Jagadheesh; Takawale, Harshvardhan; Chokhani, Yash; Bhanu, P. Veda; Soumya, J

doi:10.1109/vdat50263.2020.9190340

Cited by 7 publications

(33 citation statements)

References 15 publications

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“…After 30 percentage of link faults in the network, average network latency is decreased because the algorithms delivered packets only to the nearest destinations. But, it is increased in the proposed RL-FTR algorithm than the algorithms proposed in [7], [8] and [9]. This is because the proposed RL-FTR algorithm delivered more packets to the longer distance destinations.…”

Section: Experimental Results Analysismentioning

confidence: 93%

“…It is observed that the average network latency increased till 25-30 percentage of link faults present in the mesh network because the algorithms delivered packets using fault-tolerant routing paths that are often longer compared to the fault-free routing paths. However, the average network latency for the proposed RL-FTR algorithm is less than the algorithms proposed in [7], [8] and [9]. After 30 percentage of link faults in the network, average network latency is decreased because the algorithms delivered packets only to the nearest destinations.…”

Section: Experimental Results Analysismentioning

confidence: 97%

“…The failed links isolate some of the destinations and sources from the mesh network, which resulted in packet loss. The proposed RL-FTR algorithm delivered more packets than the algorithms proposed in [7], [8] and [9]. approach [7] approach [9] approach [8] (a) Mesh of size 4x4.…”

Section: Experimental Results Analysismentioning

confidence: 99%

“…DRL-based control policy acquires NoC behavior knowledge and operates it in optimal mode to improve energy efficiency, performance, and reliability. A Q-learning based fault-tolerant routing algorithm is proposed in [7]. The Qlearning has Q-table, which helps to take the routing path related decision.…”

Section: Related Workmentioning

confidence: 99%

“…3) Real-time behavior of the proposed RL-FTR algorithm is observed using the case studies by implementing it on FPGA. 4) Resource utilization and power analysis are reported for the proposed RL-FTR algorithm and compared it with the algorithms proposed in [7], [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning Based Fault-Tolerant Routing Algorithm for Mesh Based NoC and Its FPGA Implementation

et al. 2022

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Network-on-Chip (NoC) has emerged as the most promising on-chip interconnection framework in Multi-Processor System-on-Chips (MPSoCs) due to its efficiency and scalability. In the deep sub-micron level, NoCs are vulnerable to faults, which leads to the failure of network components such as links and routers. Failures in NoC components diminish system efficiency and reliability. This paper proposes a Reinforcement Learning based Fault-Tolerant Routing (RL-FTR) algorithm to tackle the routing issues caused by link and router faults in the mesh-based NoC architecture. The efficiency of the proposed RL-FTR algorithm is examined using System-C based cycle-accurate NoC simulator. Simulations are carried out by increasing the number of links and router faults in various sizes of mesh. Followed by simulations, real-time functioning of the proposed RL-FTR algorithm is observed using the FPGA implementation. Results of the simulation and hardware shows that the proposed RL-FTR algorithm provides an optimal routing path from the source router to the destination router.

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Section: Experimental Results Analysismentioning

confidence: 93%

Section: Experimental Results Analysismentioning

confidence: 97%