Number Systems for Deep Neural Network Architectures

Name: Number Systems for Deep Neural Network Architectures
Author: Ghada Alsuhli

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Paperback, blz. | Engels

Springer Nature Switzerland | e druk, 2024

ISBN13: 9783031381355

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Springer Nature Switzerland e druk, 2024 9783031381355

Onderdeel van serie Synthesis Lectures on Engineering, Science, and Technology

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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.

Specificaties

ISBN13:9783031381355

Taal:Engels

Bindwijze:paperback

Uitgever:Springer Nature Switzerland

Serie:Synthesis Lectures on Engineering, Science, and Technology

Inhoudsopgave

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.

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