Accelerating Error Correction Code Transformers

Matan Levy, Yoni Choukroun, Lior Wolf

Keywords: Error Correction Code, quantization, attention optimization

TL;DR: ECC Transformers cannot benefit from the existing acceleration methods designed for large Transformers. To meet efficiency demands, we propose: (1) suitable ternary quantization, (2) an optimized attention mechanism, and (3) Tanner graph-based PE.

Abstract:

Error correction codes (ECC) are crucial for ensuring reliable information transmission in communication systems. Choukroun & Wolf (2022b) recently introduced the Error Correction Code Transformer (ECCT), which has demonstrated promising performance across various transmission channels and families of codes. However, its high computational and memory demands limit its practical applications compared to traditional decoding algorithms. Achieving effective quantization of the ECCT presents significant challenges due to its inherently small architecture, since existing, very low-precision quantization techniques often lead to performance degradation in compact neural networks. In this paper, we introduce a novel acceleration method for transformer-based decoders. We first propose a ternary weight quantization method specifically designed for the ECCT, inducing a decoder with multiplication-free linear layers. We present an
optimized self-attention mechanism to reduce computational complexity via codeaware multi-heads processing. Finally, we provide positional encoding via the Tanner graph eigendecomposition, enabling a richer representation of the graph connectivity. The approach not only matches or surpasses ECCT’s performance but also significantly reduces energy consumption, memory footprint, and computational complexity. Our method brings transformer-based error correction closer to practical implementation in resource-constrained environments, achieving a 90% compression ratio and reducing arithmetic operation energy consumption by at least 224 times on modern hardware.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 3573