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.
This exciting new book covers various types of digital phase lock loops. It presents a comprehensive coverage of a new class of digital phase lock loops called the time delay tanlock loop (TDTL). It also details a number of architectures that improve the performance of the TDTL through adaptive techniques that overcome the conflicting requirements of the locking rage and speed of acquisition.
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