Alsuhli G. Number Systems for Deep Neural Network Architectures 2023

Textbook in PDF format

Explores different design aspects associated with each number system and their effects on DNN performance
Discusses the most efficient number systems for DNNs hardware realization
Describes various number systems and their usage for Deep Neural Network hardware implementation
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