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.

翻译：在新型高性能片上网络（NoC）架构中，高效的功耗管理对于降低能耗至关重要。本文提出一种名为CAFEEN的新型框架，该框架结合了基于启发式的细粒度与基于机器学习的粗粒度功耗门控技术，以实现高能效的NoC。CAFEEN在网络负载较低时采用细粒度方法，仅激活必要的NoC缓冲区；在峰值负载时则切换至粗粒度方法，利用多智能体强化学习最小化累积唤醒开销。实验结果表明，相较于最先进的NoC功耗门控框架，CAFEEN能够自适应地平衡能效与性能，在单应用负载下总能耗降低2.60倍，在多应用负载下总能耗降低4.37倍。