Titlebook: Number Systems for Deep Neural Network Architectures; Ghada Alsuhli,Vasilis Sakellariou,Thanos Stouraiti Book 2024 The Editor(s) (if appli

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书目名称Number Systems for Deep Neural Network Architectures影响因子(影响力)




书目名称Number Systems for Deep Neural Network Architectures影响因子(影响力)学科排名




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书目名称Number Systems for Deep Neural Network Architectures网络公开度学科排名




书目名称Number Systems for Deep Neural Network Architectures被引频次




书目名称Number Systems for Deep Neural Network Architectures被引频次学科排名




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书目名称Number Systems for Deep Neural Network Architectures年度引用学科排名




书目名称Number Systems for Deep Neural Network Architectures读者反馈




书目名称Number Systems for Deep Neural Network Architectures读者反馈学科排名

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978-3-031-38135-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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,DFXP for DNN Architectures, floating point systems highlighting their similarities and differences. In addition, we review existing DNN architectures that use DFXP and compare their performance. Additionally, we discuss the various factors that impact DNN performance when using DFXP and explore different approaches for determining the optimal settings of these factors.

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Ghada Alsuhli,Vasilis Sakellariou,Thanos StouraitiExplores different design aspects associated with each number system and their effects on DNN performance.Discusses the most efficient number systems for DNNs hardware realization.Describes various nu

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Conventional Number Systems for DNN Architectures,two representations and briefly discusses their utilization for implementing DNN hardware, in order to facilitate a comparison between conventional and unconventional number systems presented in subsequent chapters.

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,RNS for DNN Architectures, and multiplication become smaller and can operate on higher frequencies and with lower power consumption. In this Chapter, the basic RNS arithmetic operations and their hardware implementation are described. Moreover, RNS-based DNN architectures reported in the literature are presented and compared.