Number Systems for Deep Neural Network Architectures

Name: Number Systems for Deep Neural Network Architectures
Brand: Springer Nature Switzerland
SKU: 9783031381355
Price: 1147 CZK
Availability: InStock

Autor: Vasilis Sakellariou, Thanos Stouraitis, Mahmoud Al-Qutayri, Baker Mohammad, Hani Saleh

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer Nature Switzerland

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

1 147 Kč

This book provides readers a comprehensive introduction to alternative number systems for more effic...

Informace o knize

Autor

Vasilis Sakellariou, Thanos Stouraitis, Mahmoud Al-Qutayri, Baker Mohammad, Hani Saleh

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2024

Stránek

108

EAN

9783031381355

ISBN

3031381351

Enbook ID

46549671

Vydavatel

Springer Nature Switzerland

Hmotnost

197

Rozměry

168 x 240 x 7

Kompletní popis

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.

Mohlo by vás zajímat

Zákaznicí kteří koupili tuto knihu koupili také

Nejčastěji hledané

Categories

Authors

Publishers

Produkty

Produkty

Nejčastěji hledané

Categories

Authors

Publishers

Produkty

Produkty

Number Systems for Deep Neural Network Architectures

Informace o knize

Kompletní popis

Mohlo by vás zajímat

Zákaznicí kteří koupili tuto knihu koupili také