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arXiv · 2503.12677

RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems

Roozbeh Siyadatzadeh·Mohsen Ansari·Muhammad Shafique·Alireza Ejlali
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Abstract

Embedded systems power many modern applications and must often meet strict reliability, real-time, thermal, and power requirements. Task replication can improve reliability by duplicating a task's execution to handle transient and permanent faults, but blindly applying replication often leads to excessive overhead and higher temperatures. Existing design-time methods typically choose the number of replicas based on worst-case conditions, which can waste resources under normal operation. In this paper, we present RL-TIME, a reinforcement learning-based approach that dynamically decides the number of replicas according to actual system conditions. By considering both the reliability target and a core-level Thermal Safe Power (TSP) constraint at run-time, RL-TIME adapts the replication strategy to avoid unnecessary overhead and overheating. Experimental results show that, compared to state-of-the-art methods, RL-TIME reduces power consumption by 63%, increases schedulability by 53%, and respects TSP 72% more often.

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Roozbeh Siyadatzadeh, Mohsen Ansari, Muhammad Shafique, Alireza Ejlali. 2025-03-16. RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems. https://arxiv.org/abs/2503.12677

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