TY  - BOOK
AU  - Alsuhli,Ghada
AU  - Sakellariou,Vasilis
AU  - Saleh,Hani
AU  - Al-Qutayri,Mahmoud
AU  - Mohammad,Baker
AU  - Stouraitis,Thanos
ED  - SpringerLink (Online service)
TI  - Number Systems for Deep Neural Network Architectures
T2  - Synthesis Lectures on Engineering, Science, and Technology,
SN  - 9783031381331
AV  - TK7895.E42 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Embedded computer systems
KW  - Microprocessors
KW  - Computer architecture
KW  - Neural networks (Computer science) 
KW  - Embedded Systems
KW  - Processor Architectures
KW  - Mathematical Models of Cognitive Processes and Neural Networks
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-38133-1
ER  -