Bitte haben Sie einen Moment Geduld, wir legen Ihr Produkt in den Warenkorb.
Number Systems for Deep Neural Network Architectures
94 Seiten, Hardcover
€ 54,99
Bitte haben Sie einen Moment Geduld, wir legen Ihr Produkt in den Warenkorb.
| Reihe | Synthesis Lectures on Engineering, Science, and Technology |
|---|---|
| ISBN | 9783031381324 |
| Sprache | Englisch |
| Erscheinungsdatum | 02.09.2023 |
| Genre | Technik/Elektronik, Elektrotechnik, Nachrichtentechnik |
| Verlag | Springer International Publishing |
| Lieferzeit | Lieferbar in 11 Werktagen |
| Herstellerangaben | Anzeigen Springer Nature Customer Service Center GmbH ProductSafety@springernature.com |
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.
| Reihe | Synthesis Lectures on Engineering, Science, and Technology |
|---|---|
| ISBN | 9783031381324 |
| Sprache | Englisch |
| Erscheinungsdatum | 02.09.2023 |
| Genre | Technik/Elektronik, Elektrotechnik, Nachrichtentechnik |
| Verlag | Springer International Publishing |
| Lieferzeit | Lieferbar in 11 Werktagen |
| Herstellerangaben | Anzeigen Springer Nature Customer Service Center GmbH ProductSafety@springernature.com |
Wie gefällt Ihnen unser Shop?