Number Systems for Deep Neural Netw - E-book

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Number Systems For Deep Neural Network Architectures - ENG

Ghada Alsuhli; Vasilis Sakellariou; Hani Saleh; Mahmoud Al-Qutayri; Baker Mohammad; Thanos Stouraiti

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Sinopsis

ISBN:	9783031381331
Formato:	ePub
Idioma:	Inglés
Editorial:	Springer Nature
Tema:	Computadoras
Subtema:	Arquitectura de Sistemas general
Año de publicación:	2023-09-01

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),Â Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS).Â 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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