03667nam a22005895i 4500 978-3-031-38133-1 DE-He213 20251030110812.0 cr nn 008mamaa 230901s2024 sz | s |||| 0|eng d 9783031381331 978-3-031-38133-1 10.1007/978-3-031-38133-1 doi TK7895.E42 UKM bicssc COM092000 bisacsh UKM thema Alsuhli, Ghada. author. aut http://id.loc.gov/vocabulary/relators/aut 65856 Number Systems for Deep Neural Network Architectures [electronic resource] / by Ghada Alsuhli, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, Thanos Stouraitis. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XI, 94 p. 30 illus., 28 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Synthesis Lectures on Engineering, Science, and Technology, 2690-0327 Introduction -- Conventional number systems -- DNN architectures based on Logarithmic Number System (LNS) -- DNN architectures based on Residue Number System (RNS) -- DNN architectures based on Block Floating Point (BFP) number system -- DNN architectures based on Dynamic Fixed Point (DFXP) number system -- DNN architectures based on Posit number system. This book provides readers a comprehensive introduction to alternative number systems for more efficient representations of Deep Neural Network (DNN) data. Various number systems (conventional/unconventional) exploited for DNNs are discussed, including Floating Point (FP), Fixed Point (FXP), Logarithmic Number System (LNS), Residue Number System (RNS), Block Floating Point Number System (BFP), Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS). The authors explore the impact of these number systems on the performance and hardware design of DNNs, highlighting the challenges associated with each number system and various solutions that are proposed for addressing them. Embedded computer systems. 64625 Microprocessors. 59300 Computer architecture. 65857 Neural networks (Computer science) . 64231 Embedded Systems. 65858 Processor Architectures. 65859 Mathematical Models of Cognitive Processes and Neural Networks. 65860 Sakellariou, Vasilis. author. aut http://id.loc.gov/vocabulary/relators/aut 65861 Saleh, Hani. author. aut http://id.loc.gov/vocabulary/relators/aut 65862 Al-Qutayri, Mahmoud. author. aut http://id.loc.gov/vocabulary/relators/aut 65863 Mohammad, Baker. author. aut http://id.loc.gov/vocabulary/relators/aut 65864 Stouraitis, Thanos. author. aut http://id.loc.gov/vocabulary/relators/aut 65865 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031381324 Printed edition: 9783031381348 Printed edition: 9783031381355 Synthesis Lectures on Engineering, Science, and Technology, 2690-0327 https://doi.org/10.1007/978-3-031-38133-1 ZDB-2-SXSC 56259 56259