Number Systems for Deep Neural Network Architectures
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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.
Format:Hardcover
Language:English
ISBN:3031381327
ISBN13:9783031381324
Release Date:September 2023
Publisher:Springer
Length:94 Pages
Weight:0.85 lbs.
Dimensions:0.5" x 6.7" x 9.6"
Related Subjects
Computers Computers & Technology Engineering Math Mathematics Science & Math TechnologyCustomer Reviews
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