Isbn: 9783031381324 - number systems for deep neural network architectures (synthesis lectures on engineering, science, and technology) (2 Ergebnisse)

Sprache: Englisch
Verlag: Springer, 2023
Serie: Buch 70 von 110 - Synthesis Lectures on Engineering, Science, and Technology
- Hardcover
Anbieter: Buchpark, Trebbin, DeutschlandBuchpark
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EUR 30,59
EUR 105,00 VersandVersand von Deutschland nach USAAnzahl: 4 verfügbar
Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.…

Sprache: Englisch
Verlag: Springer, 2023
Serie: Buch 70 von 110 - Synthesis Lectures on Engineering, Science, and Technology
- Hardcover
Anbieter: Buchpark, Trebbin, DeutschlandBuchpark
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht
EUR 31,51
EUR 105,00 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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. …