CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning
<|reference_start|>CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning: In emerging high-performance Network-on-Chip (NoC) architectures, efficient power management is crucial to minimize energy consumption. We propose a novel framework called CAFEEN that employs both heuristic-based fine-grained and machine learning-based coarse-grained power-gating for energy-efficient NoCs. CAFEEN uses a fine-grained method to activate only essential NoC buffers during lower network loads. It switches to a coarse-grained method at peak loads to minimize compounding wake-up overhead using multi-agent reinforcement learning. Results show that CAFEEN adaptively balances power-efficiency with performance, reducing total energy by 2.60x for single application workloads and 4.37x for multi-application workloads, compared to state-of-the-art NoC power-gating frameworks.<|reference_end|>
@article{khan2024cafeen:,
    title={CAFEEN: A Cooperative Approach for Energy Efficient NoCs with
  Multi-Agent Reinforcement Learning},
    author={Kamil Khan, Sudeep Pasricha},
    journal={arXiv preprint arXiv:2410.07426},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07426},
    primaryClass={cs.LG cs.AI cs.AR}
}