| 1. |
- Farahnakian, Fahimeh, et al.
(författare)
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Adaptive Load Balancing in Learning-based Approaches for Many-core Embedded Systems
- 2014
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Ingår i: Journal of Supercomputing. - : Springer Science and Business Media LLC. - 0920-8542 .- 1573-0484. ; 68:3, s. 1214-1234
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Tidskriftsartikel (refereegranskat)abstract
- Adaptive routing algorithms improve network performance by distributingtraffic over the whole network. However, they require congestion information to facilitateload balancing. To provide local and global congestion information, we proposea learning method based on dual reinforcement learning approach. This informationcan be dynamically updated according to the changing traffic condition in the networkby propagating data and learning packets. We utilize a congestion detection methodwhich updates the learning rate according to the congestion level. This method calculatesthe average number of free buffer slots in each switch at specific time intervalsand compares it with maximum and minimum values. Based on the comparison result,the learning rate sets to a value between 0 and 1. If a switch gets congested, the learningrate is set to a high value, meaning that the global information is more important thanlocal. In contrast, local is more emphasized than global information in non-congestedswitches. Results show that the proposed approach achieves a significant performanceimprovement over the traditional Q-routing, DRQ-routing, DBAR and Dynamic XYalgorithms.
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| 2. |
- Farahnakian, Fahimeh, et al.
(författare)
-
Bi-LCQ: A Low-weight Clustering-based Q-learning Approach for NoCs
- 2014
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Ingår i: Microprocessors and microsystems. - : Elsevier BV. - 0141-9331 .- 1872-9436. ; 38:1, s. 64-75
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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