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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >CURE: A High-Performance, Low-Power, and Reliable Network-on-Chip Design Using Reinforcement Learning
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CURE: A High-Performance, Low-Power, and Reliable Network-on-Chip Design Using Reinforcement Learning

机译：固化：使用强化学习的高性能，低功耗和可靠的网络上的芯片设计

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摘要

We propose CURE, a deep reinforcement learning (DRL)-based NoC design framework that simultaneously reduces network latency, improves energy-efficiency, and tolerates transient errors and permanent faults. CURE has several architectural innovations and a DRL-based hardware controller to manage design complexity and optimize trade-offs. First, in CURE, we propose reversible multi-function adaptive channels (RMCs) to reduce NoC power consumption and network latency. Second, we implement a new fault-secure adaptive error correction hardware in each router to enhance reliability for both transient errors and permanent faults. Third, we propose a router power-gating and bypass design that powers off NoC components to reduce power and extend chip lifespan. Further, for the complex dynamic interactions of these techniques, we propose using DRL to train a proactive control policy to provide improved fault-tolerance, reduced power consumption, and improved performance. Simulation using the PARSEC benchmark shows that CURE reduces end-to-end packet latency by 39 percent, improves energy efficiency by 92 percent, and lowers static and dynamic power consumption by 24 and 38 percent, respectively, over conventional solutions. Using mean-time-to-failure, we show that CURE is 7.7x more reliable than the conventional NoC design.
机译：我们提出固化，一个深度加强学习（DRL）基础的NoC设计框架，同时降低了网络延迟，提高了能量效率，并容忍瞬态误差和永久性故障。 Cure有几种建筑创新和基于DRL的硬件控制器，可以管理设计复杂性并优化权衡。首先，在Cure中，我们提出了可逆的多功能自适应通道（RMC），以降低NoC功耗和网络延迟。其次，我们在每个路由器中实现了一种新的故障安全自适应纠错硬件，以增强瞬态错误和永久性故障的可靠性。第三，我们提出了一种路由器电源门控和旁路设计，可为NOC组件提供为降低电源并扩展芯片寿命。此外，对于这些技术的复杂动态相互作用，我们建议使用DRL培训主动控制策略，以提供改善的容错，降低功耗和改进的性能。使用PARSEC基准模拟显示，CURE将端到端的数据包延迟降低39％，将能效提高92％，分别降低了传统解决方案的24和38％的静态和动态功耗。使用含义延时 - 失败，我们表明固化比传统的NOC设计更可靠7.7倍。

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