NDLI: Improved Route Selection Approaches using Q-learning framework for 2D NoCs

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Improved Route Selection Approaches using Q-learning framework for 2D NoCs

Content Provider	ACM Digital Library
Author	Daneshtalab, Masoud Gaur, Manoj Singh Kumar, Manoj Gupta, Niyati Ebrahimi, Masoumeh Laxmi, Vijay Sharma, Ashish
Abstract	With the emergence of large multi-core architectures, a volume of research has been focused on distributing traffic evenly over the whole network. However, increase in traffic density may lead to congestion and subsequently degrade the performance by increased latency in the network. In this paper, we propose two novel route selection strategies for on-chip networks which are based on the Q-learning framework. The proposed strategies use variable learning rate to dynamically capture the current congestion status of the network using an additional parameter and improves the learning process to select a less congested output channel. Both the proposed selection strategies are found to adapt significantly faster to the changes in traffic load and traffic patterns by avoiding congested areas. The results demonstrate that proposed strategies achieve significant performance improvement over conventional Q-routing and its variants with slight area-overhead.
Starting Page	33
Ending Page	40
Page Count	8
File Format	PDF
ISBN	9781450334082
DOI	10.1145/2768177.2768180
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Q-learning Q-routing Congestion On-chip networks
Content Type	Text
Resource Type	Article

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