Paper
11 December 2024 Software and hardware co-design strategy of RISC-V chip for power business application
Shuhua Zhang, Chuanqi Zhao, Wei Yang, Chao Tong, Bo Gao, Lisha Gao, Chengling Jiang
Author Affiliations +
Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134452F (2024) https://doi.org/10.1117/12.3054743
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China
Abstract
With the increasing data scale and the increasing complexity of data processing requirements in the power industry, the demand for high-performance and low-power processor solutions is increasingly urgent. According to the characteristics of power business applications, this paper proposes a software and hardware co-design strategy of RISC-V (Reduced Instruction Set Computing-V) chip for power business applications. The strategy focuses on instruction set optimization, memory system optimization, parallel computing optimization and hardware accelerator design, aiming at improving the performance and energy efficiency of the chip and meeting the demand of large-scale data processing in the power field. Through case study and verification, the research results show the remarkable effects of this strategy in improving processing speed, reducing power consumption and improving energy efficiency. The research results of this paper provide an efficient processor scheme for data processing in power industry, and also provide new ideas and methods for the application of RISC-V architecture in power industry.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Shuhua Zhang, Chuanqi Zhao, Wei Yang, Chao Tong, Bo Gao, Lisha Gao, and Chengling Jiang "Software and hardware co-design strategy of RISC-V chip for power business application", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134452F (11 December 2024); https://doi.org/10.1117/12.3054743
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KEYWORDS
Design

Parallel computing

Computer hardware

Data processing

Energy efficiency

Power consumption

Computer architecture

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