论文标题
种族：改进NOC通道缓冲区自适应控制的增强学习框架
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers
论文作者
论文摘要
网络芯片（NOC）体系结构依靠缓冲区来存储FLITS，以应对数据包切换期间的Router Rouses的争夺。最近，已经提出了可逆的多功能通道（RMC）缓冲液，以同时降低功率并启用相邻路由器之间的自适应NOC缓冲。尽管自适应缓冲可以通过最大化缓冲区利用率来改善NOC性能，但控制RMC缓冲区分配需要拥堵，可扩展和主动的策略。在这项工作中，我们展示了种族，这是一种新颖的增强学习（RL）框架，它利用对网络拥塞的了解和新的奖励指标（“ falsefulls”），以帮助指导RL代理商对更好的RMC缓冲区控制决策。我们表明，对于最先进的NOC缓冲区控制政策，种族将NOC潜伏期最多减少了48.9％，最高能源消耗多达47.1％。
Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching. Recently, reversible multi-function channel (RMC) buffers have been proposed to simultaneously reduce power and enable adaptive NoC buffering between adjacent routers. While adaptive buffering can improve NoC performance by maximizing buffer utilization, controlling the RMC buffer allocations requires a congestion-aware, scalable, and proactive policy. In this work, we present RACE, a novel reinforcement learning (RL) framework that utilizes better awareness of network congestion and a new reward metric ("falsefulls") to help guide the RL agent towards better RMC buffer control decisions. We show that RACE reduces NoC latency by up to 48.9%, and energy consumption by up to 47.1% against state-of-the-art NoC buffer control policies.
