A CGP-based Efficient Approximate Multiplier with Error Compensation

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Shen:2022:ICTA,

• author = "Qiao Shen and Renyuan Zhang and Hao Zhang and Hao Cai and Bo Liu2 and Jian Xiao",
• booktitle = "2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)",
• title = "A {CGP-based} Efficient Approximate Multiplier with Error Compensation",
• year = "2022",
• pages = "48--49",
• abstract = "As one of the most promising energy-efficient paradigms in deploying Neural Network (NN) on hardware, approximate computing (AxC) has recently gained great traction to replace exact computing. This paper proposes an efficient approximate multiplier design method, which combines the Cartesian Genetic Programming (CGP)-based automatic design method and manual design method. Besides, an error compensation scheme based on the traversal search of truth table is proposed for higher-order multiplier construction. Experiments show that compared to exact multiplier, the proposed approximate multiplier can reduce the area, power consumption, and delay by 54.percent, 55.percent, and 36.8percent, respectively. It also shows superiority to the state-of-the-art approximate multiplier. In addition, when deployed in LeNet-5 for MINIST datasets, the proposed multipliers show higher efficiency than exact multiplier with comparable recognition accuracy.",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Integrated circuit technology, Power demand, Design methodology, Error compensation, Manuals, Artificial neural networks, approximate computing, multiplier",
• DOI = "doi:10.1109/ICTA56932.2022.9963083",
• ISSN = "2831-3968",
• month = oct,
• notes = "Also known as \cite{9963083}",

}

Genetic Programming entries for Qiao Shen RenYuan Zhang Hao Zhang Hao Cai Bo Liu2 Jian Xiao

Citations