Hardware-Based Linear Program Decoding with the Alternating Direction Method of Multipliers
Mitchell Wasson, Mario Milicevic, Stark C. Draper, and Glenn Gulak

TL;DR
This paper introduces a hardware implementation of LP decoding for binary linear codes using ADMM, demonstrating near-optimal error correction performance and resource efficiency on FPGA, with potential for high-speed ASIC development.
Contribution
It presents a novel hardware-based ADMM-LP decoding algorithm with efficient projection methods and FPGA implementation insights for scalable, high-performance error correction.
Findings
Achieves FER within 0.5dB of double-precision implementations with 10-bit messages
Demonstrates FPGA implementation with practical resource estimates
Outlines pathways for ASIC development for gigabit per second throughput
Abstract
We present a hardware-based implementation of Linear Program (LP) decoding for binary linear codes. LP decoding frames error-correction as an optimization problem. In contrast, variants of Belief Propagation (BP) decoding frame error-correction as a problem of graphical inference. LP decoding has several advantages over BP-based methods, including convergence guarantees and better error-rate performance in high-reliability channels. The latter makes LP decoding attractive for optical transport and storage applications. However, LP decoding, when implemented with general solvers, does not scale to large blocklengths and is not suitable for a parallelized implementation in hardware. It has been recently shown that the Alternating Direction Method of Multipliers (ADMM) can be applied to decompose the LP decoding problem. The result is a message-passing algorithm with a structure very…
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