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Mamba-based neural decoder offers scalable solution for error-correcting codes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new neural decoder called MMPD, which utilizes Mamba state-space blocks to efficiently process long error-correcting codes. This attention-free approach significantly reduces memory and computational costs compared to previous attention-based models. In tests on LDPC codes, MMPD demonstrated a notable performance gain and a substantial reduction in memory usage, making it suitable for practical, long-code applications. AI

IMPACT Introduces a more memory-efficient architecture for processing long error-correcting codes, potentially improving communication reliability in various systems.

RANK_REASON The cluster contains a research paper detailing a new model architecture for error-correcting codes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Mamba-based neural decoder offers scalable solution for error-correcting codes

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The cluster contains a research paper detailing a new model architecture for error-correcting codes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dmitry Artemasov ·

    Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes

    Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown strong performance for short codes, but their scalability to longer codes is limited by the quadratic memory and computational cost of atten…

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