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Number Systems for Deep Neural Network Architectures

Saleh, Hani   Baker Mohammad   Alsuhli, Ghada   Sakellariou, Vasilis   Stouraitis, Thanos   Al-Qutayri, Mahmoud   + weitere anzeigen
Englisch | Hardcover

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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Produktinformationen "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.

H | B | T | Gramm
246 mm | 173 mm | 12 mm | 314 gr
Erscheinungsjahr
2023
FSK
0
Ausgabe
Hardcover
Verlag
Springer
ISBN-10
3031381327
ISBN-13
9783031381324
Autor
Alsuhli, Ghada; Saleh, Hani; Baker Mohammad; Stouraitis, Thanos; Sakellariou, Vasilis; Al-Qutayri, Mahmoud
Sprache
Englisch
Seitenanzahl
108
Themen
Mathematische Modellierung, Eingebettete Systeme, Rechnerarchitektur und Logik-Entwurf
Verantwortliche Person gemäß Art. 16 GPSR
Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115, Heidelberg, DE, ProductSafety@springernature.com

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Number Systems for Deep Neural Network Architectures
Number Systems for Deep Neural Network Architectures
Saleh, Hani
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.

Buch | Englisch

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