Specialized Scalar and SIMD Instructions for Error Correction Codes Decoding on RISC-V Processors
Year: 2025
Type: article
Abstract: The rapid deployment of Internet-of-Things (IoT) devices for a few years has been impressive, and the progressive deployment of 5G will accelerate things even further. Indeed, this standard opens the door to a new generation of standards aimed at a convergence of networks and communication protocols (WiFi, LTE, 4G etc.). This results in the need for flexible implementations of different families of codes, such as, LPDC, NB-LDPC, turbo codes and polar codes. In this context, the work presented in this article proposes the design of a flexible instruction set processor for an IoT context. The objective is to improve the performance level of low-complexity processor cores through instruction set extensions forError Correction Code(ECC) decoding. The approach discussed is supported by experimental results obtained based on a RISC-V architecture to which specific instruction sets have been added. The results demonstrate a reduction in the required processing clock cycles up to 44.1% for polar codes, 39.2% for LDPC codes, 21.8% for NB-LDPC codes, and 24.3% for turbo codes (4G LTE) codes with a classicalSingle Instruction Single Data(SISD) approach. Moreover,Single Instruction Multiple Data(SIMD) parallelization strategy enables execution time savings that are far more impressive. The number of clock cycles required to decode a data bit is reduced by 65.6% to 76.9%, with a limited hardware over-cost from 0.6% to 34% (depending on the error correction code family and the targeted RISC-V core).
Source: IEEE Access
Institutions: Université de Bordeaux, Université de Bretagne Sud, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, Laboratoire de l'Intégration du Matériau au Système, Institut Polytechnique de Bordeaux, Techniques of Informatics and Microelectronics for Integrated Systems Architecture, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, Université de Rennes
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4406171760