Alsuhli, Ghada. creator author. aut http://id.loc.gov/vocabulary/relators/aut Sakellariou, Vasilis. author. aut http://id.loc.gov/vocabulary/relators/aut Saleh, Hani. author. aut http://id.loc.gov/vocabulary/relators/aut Al-Qutayri, Mahmoud. author. aut http://id.loc.gov/vocabulary/relators/aut Mohammad, Baker. author. aut http://id.loc.gov/vocabulary/relators/aut Stouraitis, Thanos. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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access XI, 94 p. 30 illus., 28 illus. in color. online resource. 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. 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. by Ghada Alsuhli, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, Thanos Stouraitis. Embedded computer systems Microprocessors Computer architecture Neural networks (Computer science)  Embedded Systems Processor Architectures Mathematical Models of Cognitive Processes and Neural Networks TK7895.E42 9783031381331 https://doi.org/10.1007/978-3-031-38133-1 https://doi.org/10.1007/978-3-031-38133-1 230901 20251030110812.0 978-3-031-38133-1