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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鬧劇

吝嗇性

Processes

Albinism

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

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使閉塞

,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.

印第安人

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

Oscillate

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.

Employee

,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.