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Название: Number Systems for Deep Neural Network Architectures
Автор: Ghada Alsuhli, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri
Издательство: Springer
Год: 2024
Страниц: 100
Язык: английский
Формат: pdf (true), epub
Размер: 11.2 MB
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. During the past decade, DNNs have shown outstanding performance in a myriad of Artificial Intelligence (AI) applications. Since their success in both speech and image recognition, great attention has been drawn to DNNs from academia and industry, which subsequently led to a wide range of products that utilize them.
Автор: Ghada Alsuhli, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri
Издательство: Springer
Год: 2024
Страниц: 100
Язык: английский
Формат: pdf (true), epub
Размер: 11.2 MB
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. During the past decade, DNNs have shown outstanding performance in a myriad of Artificial Intelligence (AI) applications. Since their success in both speech and image recognition, great attention has been drawn to DNNs from academia and industry, which subsequently led to a wide range of products that utilize them.