{"title":"LCAM: Low-Cost Approximate Multiplier Design on FPGA","authors":"Mingyu Shu, Qiang Liu","doi":"10.1109/ICFPT56656.2022.9974375","DOIUrl":null,"url":null,"abstract":"Approximate multiplier is a computing unit, which reduces resource and power by sacrificing computational accuracy, and is widely used in fields such as image processing and deep neural networks. In this paper, a low-cost $\\mathbf{8}\\times \\mathbf{8}$ unsigned approximate multiplier is proposed by considering FPGA architectural features. A stage-aware most significant bits (MSBs) selection scheme is designed for error recovery to trade off accuracy and resource usage. The proposed multiplier saves up to 19.7% LUT utilization while the accuracy only decreases 4%, compared to the accurate Xilinx multiplier IP.","PeriodicalId":239314,"journal":{"name":"2022 International Conference on Field-Programmable Technology (ICFPT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Field-Programmable Technology (ICFPT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFPT56656.2022.9974375","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Approximate multiplier is a computing unit, which reduces resource and power by sacrificing computational accuracy, and is widely used in fields such as image processing and deep neural networks. In this paper, a low-cost $\mathbf{8}\times \mathbf{8}$ unsigned approximate multiplier is proposed by considering FPGA architectural features. A stage-aware most significant bits (MSBs) selection scheme is designed for error recovery to trade off accuracy and resource usage. The proposed multiplier saves up to 19.7% LUT utilization while the accuracy only decreases 4%, compared to the accurate Xilinx multiplier IP.