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Accelerating Neural BP-Based Decoder Using Coded Distributed Computing
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DOI：10.1109/TVT.2024.3391836.png)
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Abstract
En 中文
While neural BP-based (NBP) decoders exhibit superior error correction performance compared to belief-propagation (BP) decoders, the NBP decoder's high computational and memory requirements impede its practical deployment in communication systems. To overcome this challenge, we propose a Coded Neural BP (CNBP) scheme to accelerate the NBP decoder in distributed environments, while considering storage constraints and providing resilience to stragglers. The key idea is to reformulate the primary operations of the NBP decoder as matrix-vector multiplications by introducing weight matrices and transformations. Based on this, the acceleration of the NBP decoder is achieved by speeding up matrix-vector multiplications using coded distributed computing. Extensive experiments conducted on Amazon EC2 cluster demonstrate that CNBP achieves notable acceleration and scalability performance without any loss in error correction performance.
Keywords:
BP-based decoder
coding theory
distributed computing
neural network
straggler resilience
BP-based decoder
coding theory
distributed computing
neural network
straggler resilience
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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