Deep Reinforcement Learning for System-on-Chip: Myths and Realities. (arXiv:2207.14595v1 [cs.LG])

Neural schedulers based on deep reinforcement learning (DRL) have shown
considerable potential for solving real-world resource allocation problems, as
they have demonstrated significant performance gain in the domain of cluster
computing. In this paper, we investigate the feasibility of neural schedulers
for the domain of System-on-Chip (SoC) resource allocation through extensive
experiments and comparison with non-neural, heuristic schedulers. The key
finding is three-fold. First, neural schedulers designed for cluster computing
domain do not work well for SoC due to i) …

arxiv chip learning lg reinforcement reinforcement learning system-on-chip