UID:
almahu_9949744360002882
Format:
XI, 94 p. 30 illus., 28 illus. in color.
,
online resource.
Edition:
1st ed. 2024.
ISBN:
9783031381331
Series Statement:
Synthesis Lectures on Engineering, Science, and Technology,
Content:
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.
Note:
Introduction -- Conventional number systems -- DNN architectures based on Logarithmic Number System (LNS) -- DNN architectures based on Residue Number System (RNS) -- DNN architectures based on Block Floating Point (BFP) number system -- DNN architectures based on Dynamic Fixed Point (DFXP) number system -- DNN architectures based on Posit number system.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031381324
Additional Edition:
Printed edition: ISBN 9783031381348
Additional Edition:
Printed edition: ISBN 9783031381355
Language:
English
DOI:
10.1007/978-3-031-38133-1
URL:
https://doi.org/10.1007/978-3-031-38133-1
