Number Systems for Deep Neural Network Architectures (1st ed. 2024) (Synthesis Lectures on Engineering, Science, and Technology)

By: Ghada Alsuhli and Vasilis Sakellariou and Hani Saleh and Mahmoud Al-Qutayri and Baker Mohammad and Thanos Stouraitis

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Synopsis: 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.

Book Quality:: Publisher Quality
ISBN-13:: 9783031381331
Related ISBNs:: 9783031381324
Publisher:: Springer Nature Switzerland
Date of Addition:: 10/20/23
Copyrighted By:: The Editor
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Technology, Mathematics and Statistics
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.