Alsuhli, Ghada. <br/><br/>
Number Systems for Deep Neural Network Architectures  [electronic resource] / 
by Ghada Alsuhli, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, Thanos Stouraitis. 
 - 1st ed. 2024. 
 - XI, 94 p. 30 illus., 28 illus. in color.  online resource. 
 - Synthesis Lectures on Engineering, Science, and Technology,  2690-0327 .
 - Synthesis Lectures on Engineering, Science, and Technology, .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783031381331 
<br/><br/><label>Standard No.: </label>10.1007/978-3-031-38133-1  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/><br/><br/><br/><br/><br/><br/>
<br/><br/><label>LC Class. No.: </label>TK7895.E42