Bi-LCQ: A Low-weight Clustering-based Q-learning Approach for NoCs

• 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Keyword

Adaptive routing algorithm

Congestion-aware routing algorithm

Network-on-Chip

Q-learning and Q-routing approaches

Publication and Content Type

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