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.

Number Systems for Deep Neural Network Architectures features in the following genres: Embedded systems, Mathematical modelling, Computer architecture and logic design

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Number Systems for Deep Neural Network Architectures was written by Ghada Alsuhli and published by Springer an imprint of Springer Nature Switzerland

Number Systems for Deep Neural Network Architectures has 94 pages

Yes it is part of Synthesis Lectures on Engineering, Science, and Technology series

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