Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-likelihood-each-notable-amplitude spiking-neural-network (ELENA-SNN) decoder, a novel decoding algorithm for low-density parity-check (LDPC) codes. The decoder integrates SNNs into belief propagation (BP) decoding by approximating the check node (CN) update equation using SNNs. However, when decoding LDPC codes with a small variable node(VN) degree, the approximation gets too rough, and the ELENA-SNN decoder does not yield good results. This paper introduces the multi-level ELENA-SNN (ML-ELENA-SNN) decoder, which is an extension of the ELENA-SNN decoder. Instead of a single SNN approximating the CN update, multiple SNNs are applied in parallel, resulting in a higher resolution and higher dynamic range of the exchanged messages. We show that the ML-ELENA-SNN decoder performs similarly to the ubiquitous normalized min-sum decoder for the (38400, 30720) regular LDPC code with a VN degree of dv = 3 and a CN degree of dc = 15.

翻译：脉冲神经网络通过模仿生物神经元的基于事件的行为，有望实现高能效的数据处理。在先前的工作中，我们引入了扩大似然-显著幅度脉冲神经网络译码器，这是一种用于低密度奇偶校验码的新型译码算法。该译码器通过使用SNN近似校验节点更新方程，将SNN集成到置信传播译码中。然而，当译码变量节点度较小的LDPC码时，该近似变得过于粗糙，导致ELENA-SNN译码器无法获得良好的性能。本文介绍了多级ELENA-SNN译码器，它是ELENA-SNN译码器的扩展。该译码器不再使用单个SNN来近似CN更新，而是并行应用多个SNN，从而提高了所交换消息的分辨率和动态范围。我们证明，对于VN度dv = 3、CN度dc = 15的(38400, 30720)规则LDPC码，ML-ELENA-SNN译码器的性能与广泛使用的归一化最小和译码器相当。