Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	September 2023
Genre:	Naturwissensch., Medizin, Technik
	deepneuralnetworkaccelerators / deepneuralnetworkarchitectures / deepneuralnetworkhardwareimplementation / deepneuralnetworknumberrepresentation / Eingebettete Systeme / numbersystemsfordeepneuralnetworkhardwareimplementation / Rechnerarchitektur und Logik-Entwurf
ISBN:	9783031381324
EAN-Code:	9783031381324
Verlag:	Springer
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 246 mm / B 173 mm / D 12 mm
Gewicht:	372 gr
Seiten:	108
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.