Number Systems for Deep Neural Network Architectures

Number Systems for Deep Neural Network Architectures

by Ghada AlsuhliVasilis Sakellariou Hani Saleh and others
Epub (Kobo), Epub (Adobe)
Publication Date: 01/09/2023

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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.

ISBN:
9783031381331
9783031381331
Category:
Computer architecture & logic design
Format:
Epub (Kobo), Epub (Adobe)
Publication Date:
01-09-2023
Language:
English
Publisher:
Springer Nature Switzerland

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