Work-in-Process: Error-Compensation-Based Energy-Efficient MAC Unit for CNNs

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Xu:2023:CASES,

• author = "Xingyu Xu and Qingwen Wei and Yang Zhang and Hao Cai and Bo Liu",
• booktitle = "2023 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)",
• title = "Work-in-Process: Error-Compensation-Based Energy-Efficient {MAC} Unit for {CNNs}",
• year = "2023",
• pages = "3--4",
• abstract = "Approximate circuits sacrifice accuracy in exchange for energy efficiency and have been widely used in hardware deployment of neural networks (NNs). Since convolution accounts for most of the power consumption in NNs, it is necessary to design an approximate multiplication and accumulation (MAC) unit which improve the energy efficiency of hardware with ignorable accuracy loss. an error-compensation-based energy-efficient MAC unit is proposed in which approximate multipliers are designed by Boolean matrix factorization and approximate adders are generated by Cartesian genetic programming. The proposed MAC unit is conducted on CIFAR10 using ResNet-18, where PDP is reduced by 58.8percent with an accuracy loss of 0.81percent.",
• keywords = "genetic algorithms, genetic programming, Power demand, Embedded systems, Computer aided software engineering, Convolution, Artificial neural networks, ANN, Computer architecture, Approximate computing, Energy efficiency, Error compensation",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10316208",
• ISSN = "2643-1726",
• month = sep,
• notes = "Also known as \cite{10316208}",

}

Genetic Programming entries for Xingyu Xu Qingwen Wei Yang Zhang Hao Cai Bo Liu

Citations