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TLP-LDPC: Three-Level Parallel FPGA Architecture for Fast Prototyping of LDPC Decoder Using High-Level Synthesis

Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
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Abstract

Low-Density Parity-heck Codes (LDPC) with excellent error-correction capabilities have been widely used in both data communication and storage fields, to construct reliable cyber-physical systems that are resilient to real-world noises. Fast prototyping field-programmable gate array (FPGA)-based decoder is essential to achieve high decoding performance while accelerating the development process. This paper proposes a three-level parallel architecture, TLP-LDPC, to achieve high throughput by fully exploiting the characteristics of both LDPC and underlying hardware while effectively scaling to large-size FPGA platforms. The three-level parallel architecture contains a low-level decoding unit, a mid-level multi-unit decoding core, and a high-level multi-core decoder. The low-level decoding unit is a basic LDPC computation component that effectively combines the features of the LDPC algorithm and hardware with the specific structure (e.g., Look-Up-Table, LUT) of the FPGA and eliminates potential data conflicts. The mid-level decoding core integrates the input/output and multiple decoding units in a well-balancing pipelined fashion. The top-level multi-core architecture conveniently makes full use of board-level resources to improve the overall throughput. We develop an LDPC C++ code with dedicated pragmas and leverage HLS tools to implement the TLP-LDPC architecture. Experimental results show that TLP-LDPC achieves 9.63 Gbps end-to-end decoding throughput on a Xilinx Alveo U50 platform, 3.9x higher than existing HLS-based FPGA implementations.

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Journal of Computer Science and Technology
Pages 1290-1306

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Cite this article:
Zhang Y-F, Sun L, Cao Q. TLP-LDPC: Three-Level Parallel FPGA Architecture for Fast Prototyping of LDPC Decoder Using High-Level Synthesis. Journal of Computer Science and Technology, 2022, 37(6): 1290-1306. https://doi.org/10.1007/s11390-022-1499-9

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Received: 06 April 2021
Accepted: 12 April 2022
Published: 30 November 2022
©Institute of Computing Technology, Chinese Academy of Sciences 2022