Toward More Efficient NoC Arbitration : A Deep Reinforcement Learning Approach
Toward More Efficient NoC Arbitration : A Deep Reinforcement Learning Approach
复制标题
实现更高效的 NoC 仲裁：深度强化学习方法
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
G. Loh
中科院分区：
文献类型：
--
作者：
Jieming Yin;Shuai Che;M. Oskin;G. Loh
片上网络（NoC）是各种片上组件共享的关键资源。有效的NoC仲裁政策对于提供全局公平和提高系统性能至关重要。在这项初步工作中，我们展示了利用深度强化学习来指导更有效的NoC仲裁策略设计的想法。我们将仲裁与自我学习决策过程联系起来。结果表明，深度强化学习方法可以有效地减少数据包延迟，并具有识别可用于更实际硬件设计的有趣特征的潜力。
The network on-chip (NoC) is a critical resource shared by various on-chip components. An efficient NoC arbitration policy is crucial in providing global fairness and improving system performance. In this preliminary work, we demonstrate an idea of utilizing deep reinforcement learning to guide the design of more efficient NoC arbitration policies. We relate arbitration to a self-learning decision making process. Results show that the deep reinforcement learning approach can effectively reduce packet latency and has potential for identifying interesting features that could be utilized in more practical hardware designs.
用于重用预测的感知器学习
DOI：
10.1109/micro.2016.7783705
发表时间：
2016
期刊：
2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO
影响因子：
--
作者：
Teran, Elvira;Wang, Zhe;Jimenez, Daniel A.
通讯作者：
Jimenez, Daniel A.