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portada Number Systems for Deep Neural Network Architectures
Type
Physical Book
Author
Publisher
Language
English
Pages
94
Format
Hardcover
Dimensions
24.4x17x1.2 cm
Weight
0.39 kg.
ISBN13
9783031381324
Categories

Number Systems for Deep Neural Network Architectures

Hani Saleh (Author) · Ghada Alsuhli (Author) · Vasilis Sakellariou (Author) · Springer · Hardcover

Number Systems for Deep Neural Network Architectures - Alsuhli, Ghada ; Sakellariou, Vasilis ; Saleh, Hani

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Synopsis "Number Systems for Deep Neural Network Architectures"

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.

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