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Bi-LCQ: A Low-weight Clustering-based Q-learning Approach for NoCs
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2014 (English)In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, ISSN 0141-9331, Vol. 38, no 1, p. 64-75Article in journal (Refereed) Published
Abstract [en]

Network congestion has a negative impact on the performance of on-chip networks due to the increasedpacket latency. Many congestion-aware routing algorithms have been developed to alleviate trafficcongestion over the network. In this paper, we propose a congestion-aware routing algorithm basedon the Q-learning approach for avoiding congested areas in the network. By using the learning method,local and global congestion information of the network is provided for each switch. This information canbe dynamically updated, when a switch receives a packet. However, Q-learning approach suffers fromhigh area overhead in NoCs due to the need for a large routing table in each switch. In order to reducethe area overhead, we also present a clustering approach that decreases the number of routing tablesby the factor of 4. Results show that the proposed approach achieves a significant performance improvementover the traditional Q-learning, C-routing, DBAR and Dynamic XY algorithms.

Place, publisher, year, edition, pages
2014. Vol. 38, no 1, p. 64-75
Keywords [en]
Adaptive routing algorithm, Congestion-aware routing algorithm, Network-on-Chip, Q-learning and Q-routing approaches
National Category
Embedded Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-254864DOI: 10.1016/j.micpro.2013.11.008OAI: oai:DiVA.org:kth-254864DiVA, id: diva2:1335732
Note

QC 20190819

Available from: 2019-07-07 Created: 2019-07-07 Last updated: 2019-08-19Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.1016/j.micpro.2013.11.008

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Ebrahimi, Masoumeh

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