RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems
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.
Cite
@article{arxiv.2503.12677,
title = {RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems},
author = {Roozbeh Siyadatzadeh and Mohsen Ansari and Muhammad Shafique and Alireza Ejlali},
journal= {arXiv preprint arXiv:2503.12677},
year = {2025}
}