# Neural Offset Min-Sum Decoding

## TL;DR

This paper introduces a hardware-efficient neural decoding method for error-correcting codes that uses learnable offsets in min-sum decoding, achieving near-multiplicative performance with fewer computations.

## Contribution

It proposes a multiplication-free, learnable offset min-sum decoding algorithm that improves error correction while being more suitable for hardware implementation.

## Key findings

- Achieves within 0.1 dB of multiplicative neural decoding performance.
- Outperforms traditional belief propagation by up to 1 dB.
- Reduces parameter count and computational complexity.

## Abstract

Recently, it was shown that if multiplicative weights are assigned to the edges of a Tanner graph used in belief propagation decoding, it is possible to use deep learning techniques to find values for the weights which improve the error-correction performance of the decoder. Unfortunately, this approach requires many multiplications, which are generally expensive operations. In this paper, we suggest a more hardware-friendly approach in which offset min-sum decoding is augmented with learnable offset parameters. Our method uses no multiplications and has a parameter count less than half that of the multiplicative algorithm. This both speeds up training and provides a feasible path to hardware architectures. After describing our method, we compare the performance of the two neural decoding algorithms and show that our method achieves error-correction performance within 0.1 dB of the multiplicative approach and as much as 1 dB better than traditional belief propagation for the codes under consideration.