用多智能体强化学习实现NoC动态节能，能效提升超4倍。
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning
- 分层控制：低负载时细粒度激活必要缓冲区，高负载时粗粒度用强化学习调度
- 单应用节能2.6倍，多应用节能4.37倍，优于现有框架
- 适合高密度芯片设计，尤其对多任务并行场景有显著优势
在新兴的高性能片上网络（NoC）架构中，高效的功耗管理对降低能耗至关重要。本文提出一种名为CAFEEN的新框架，结合启发式细粒度与机器学习粗粒度的电源门控策略，实现节能型NoC。当网络负载较低时，采用细粒度方法仅激活必要的NoC缓冲区；在峰值负载时切换至粗粒度方法，借助多智能体强化学习最小化唤醒开销。实验结果表明，CAFEEN能自适应平衡能效与性能，在单应用工作负载下总能耗降低2.60倍，在多应用工作负载下降低4.37倍，优于当前最先进的NoC电源门控框架。
原文摘要 · Abstract (English)
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.
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