Compressors Evolution based High Speed and Energy Efficient Approximate Signed Multiplier

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@InProceedings{Xie:2022:ICSICT,

• author = "Na Xie and Renyuan Zhang and Han Yan and Chonghang Xie and Hao Zhang and Hao Cai and Bo Liu2",
• title = "Compressors Evolution based High Speed and Energy Efficient Approximate Signed Multiplier",
• booktitle = "2022 IEEE 16th International Conference on Solid-State \& Integrated Circuit Technology (ICSICT)",
• year = "2022",
• abstract = "Among various energy improvement techniques, approximate computing can reduce power, area and delay at the cost of calculation accuracy losses. In this paper, we propose an approximate signed multiplier design with a low power consumption and a short critical path using Cartesian Genetic Programming (CGP) algorithm and a compressors evolution method to obtain more suitable approximate 3-2 compressors for partial products (PPs) accumulation to compensate the error generated by CGP. Experimental results show that the proposed signed multiplier reduces delay by 4percent, area by 2percent, power consumption by 2percent and the power delay product (PDP) by 56.percent. The NMED of proposed multiplier and recognition accuracy increased from 0.88percent to 0.73percent and 87.2percent to 92.6percent respectively through compressors evolution method. The proposed multiplier is also evaluated in a CRNN-based keyword spotting (KWS) system which brought high efficiency and little accuracy loss.",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Integrated circuit technology, Power demand, Costs, Circuit optimization, Compressors, Energy efficiency, Approximate signed multiplier, KWS",
• DOI = "doi:10.1109/ICSICT55466.2022.9963435",
• month = oct,
• notes = "Also known as \cite{9963435}",

}

Genetic Programming entries for Na Xie RenYuan Zhang Han Yan Chonghang Xie Hao Zhang Hao Cai Bo Liu2

Citations