On the Decoding Complexity of Error Correction Code Transformer

Belief propagation (BP) decoding achieves near-optimal performance with large block lengths but faces performance degradation with short blocks. Recent advances in deep learning have spurred research into neural decoders to overcome the limitations of BP decoding. The error correction code transformer (ECCT) offers state-of-the-art decoding performance, yet it demands substantial computational complexity and memory usage. This paper analyzes the hardware complexity of ECCT compared to the basic neural decoder, neural BP (NBP). We also explore strategies for model simplification, showing that applying quantization techniques can reduce resource overhead while maintaining high performance. This paper highlights the potential for advancing neural decoders to achieve greater efficiency and performance.