Proceedings ArticleDOI
Q-learning based congestion-aware routing algorithm for on-chip network
Fahimeh Farahnakian,Masoumeh Ebrahimi,Masoud Daneshtalab,Pasi Liljeberg,Juha Plosila +4 more
- pp 1-7
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TLDR
Experimental results with synthetic test cases demonstrate that the on-chip network utilizing the proposed reinforcement learning method, Q-learning, outperforms a conventional scheme, Dynamic XY, with a 12% of area overhead.Abstract:
Network congestion can limit performance of NoC due to increased transmission latency and power consumption. Congestion-aware adaptive routing can greatly improve the network performance by balancing the traffic load over the network. In this paper, we present a reinforcement learning method, Q-learning, for NoC to alleviate congestion in the network. In the proposed method, local and nonlocal congestion information is propagated over the network utilizing learning packets. This learning approach results in better routing decisions due to up-to-date and more reliable congestion values. According to this congestion information, a path is chosen for a packet which is less congested. Experimental results with synthetic test cases demonstrate that the on-chip network utilizing the proposed method outperforms a conventional scheme, Dynamic XY, (28% for uniform traffic and 17% for hotspot traffic) with a 12% of area overhead.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Proceedings ArticleDOI
Congestion prevention mechanism based on Q-leaning for efficient routing in SDN
TL;DR: This paper shows the network congestion can be improved by reselecting the path and changing Flow Table using predefined threshold and Q-learning routing algorithm and proposes a mechanism to prevent network congestion based on Q- learning for efficient routing in SDN.
Proceedings ArticleDOI
Adaptive reinforcement learning method for networks-on-chip
TL;DR: Experimental results for different traffic patterns and network loads show that the proposed congestion-aware routing algorithm improves the network performance compared with the standard Q-routing, DRQ- routing, and Dynamic XY-routed algorithms.
Journal ArticleDOI
Efficient Routing Protocol Based on Reinforcement Learning for Magnetic Induction Underwater Sensor Networks
TL;DR: This paper proposes an efficient routing protocol based on reinforcement learning, in particular, the Q-learning that aims to investigate the resource management in the hierarchical networks and shows that the proposed routing approach outperforms the conventional protocol in extending the network lifetime and reducing the transmission delay.
Proceedings ArticleDOI
Machine learning enabled power-aware Network-on-Chip design
TL;DR: This paper proposes LESSON (Learning Enabled Sleepy Storage Links and Routers in NoCs) to reduce both static and dynamic power consumption by power-gating the links and routers at low network utilization and moving the data storage from within the routers to the links at high network utilization.
References
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Proceedings ArticleDOI
Route packets, not wires: on-chip interconnection networks
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TL;DR: This paper introduces the concept of on-chip networks, sketches a simple network, and discusses some challenges in the architecture and design of these networks.