Optical networks-on-chips (NoCs) based on silicon photonics have been proposed as an emerging communication architecture for many-core chip multiprocessors. However, the thermal sensitivity of silicon photonics is one of the major challenges. $Q$ -learning-based adaptive routing has been proposed in related work to mitigate the thermal issue. However, table overhead of the traditional table-based $Q$ -routing would scale up quickly with the increase of network size. In this article, we propose a table-free approximate $Q$ -learning-based thermal-aware adaptive routing to find optimal low-loss paths in the presence of on-chip temperature variations. The simulation results show that the proposed table-free approximate $Q$ -learning-based adaptive routing can converge faster and it can achieve similar optimization effect as compared to the best optimization effect of the traditional table-based $Q$ -routing. The performance gap between the proposed approximation method and the traditional table-based $Q$ -routing expands when the network size increases.

中文翻译：

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基于无表近似 Q 学习的光学 NoC 的热感知自适应路由

基于硅光子学的片上光网络 (NoC) 已被提议作为一种用于众核芯片多处理器的新兴通信架构。然而，硅光子学的热敏感性是主要挑战之一。 $Q$ 在相关工作中已经提出了基于学习的自适应路由来缓解热问题。然而，传统的基于表的表开销 $Q$ -路由会随着网络规模的增加而迅速扩大。在本文中，我们提出了一种无表近似 $Q$ -基于学习的热感知自适应路由，以在存在片上温度变化的情况下找到最佳的低损耗路径。仿真结果表明，所提出的无表近似 $Q$ -基于学习的自适应路由可以更快地收敛，并且可以达到与传统基于表的最佳优化效果相似的优化效果 $Q$ -路由。所提出的近似方法与传统的基于表的方法之间的性能差距 $Q$ -路由随着网络规模的增加而扩展。