Number Systems for Deep Neural Network Architectures
Alsuhli, Ghada, Sakellariou, Vasilis, Saleh, Hani, Al-Qutayri, Mahmoud, Mohammad, Baker, Stouraitis, Thanos
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.
Kategorie:
Rok:
2023
Wydanie:
1
Wydawnictwo:
Springer
Język:
english
Strony:
205
ISBN 10:
3031381335
ISBN 13:
9783031381331
Serie:
Synthesis Lectures on Engineering, Science, and Technology
Plik:
EPUB, 9.53 MB
IPFS:
,
english, 2023
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