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(Ebook) Number Systems for Deep Neural Network Architectures by Alsuhli, Ghada, Sakellariou, Vasilis, Saleh, Hani, Al-Qutayri, Mahmoud, Mohammad, Baker, Stouraitis, Thanos ISBN 9783031381324, 9783031381331, 3031381327, 3031381335

  • SKU: EBN-55471892
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5.0

38 reviews
Instant download (eBook) Number Systems for Deep Neural Network Architectures after payment.
Authors:Alsuhli, Ghada, Sakellariou, Vasilis, Saleh, Hani, Al-Qutayri, Mahmoud, Mohammad, Baker, Stouraitis, Thanos
Pages:105 pages.
Year:2023
Editon:1
Publisher:Springer
Language:english
File Size:2.57 MB
Format:pdf
ISBNS:9783031381324, 9783031381331, 3031381327, 3031381335
Categories: Ebooks

Product desciption

(Ebook) Number Systems for Deep Neural Network Architectures by Alsuhli, Ghada, Sakellariou, Vasilis, Saleh, Hani, Al-Qutayri, Mahmoud, Mohammad, Baker, Stouraitis, Thanos ISBN 9783031381324, 9783031381331, 3031381327, 3031381335

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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