國立虎尾科技大學 |

Analog current-mode computational circuits for artificial neural networks

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Analog current-mode computational circuits for artificial neural networks/ by Cosmin Radu Popa.
Author:	Popa, Cosmin Radu.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxiii, 397 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-032-03989-7
ISBN:	9783032039897

Analog current-mode computational circuits for artificial neural networks
Popa, Cosmin Radu.

Analog current-mode computational circuits for artificial neural networks[electronic resource] /by Cosmin Radu Popa. - Cham :Springer Nature Switzerland :2025. - xxiii, 397 p. :ill., digital ;24 cm. - Analog circuits and signal processing,2197-1854. - Analog circuits and signal processing..

Introduction -- Superior-order approximation functions for generating sigmoidal activation functions -- Superior-order approximation functions for generating radial basis activation functions -- Superior-order approximation functions for artificial neural networks applications -- Analysis and design of analog function synthesizers for implmenting sigmoidal activation functions -- Analysis and design of analog function synthesizers for generating radial basis activation functions -- Analysis and design of analog function synthesizers for artificial neural networks applications -- Low-voltage low-power current-mode CMOS computational circuits for implementing activation functions -- Conclusions.

This book discusses in detail low-voltage low-power designs for minimizing the hardware resources required by neural network implementations. The novel method presented in this book for an accurate realization of activation functions for artificial neural networks (ANNs), is based on specific superior-order approximation functions. The author describes analog implementations in CMOS technology to increase the speed of operation, while reducing the hardware resources required for obtaining these approximation functions. Original architectures presented in this book, used for implementing previous CMOS computational structures, allow for operation independent of technological errors and temperature variations. SPICE simulations confirm the theoretically estimated results for previously presented CMOS computational structures, developed for ANNs and artificial intelligence applications.

ISBN: 9783032039897

Standard No.: 10.1007/978-3-032-03989-7doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Analog current-mode computational circuits for artificial neural networks
LDR:02646nam a22003375a 4500 001 1171717
003 DE-He213
005 20251107114906.0
006 m d
007 cr nn 008maaau
008 260504s2025 sz s 0 eng d
020 $a 9783032039897 $q (electronic bk.)
020 $a 9783032039880 $q (paper)
024 7 $a 10.1007/978-3-032-03989-7 $2 doi
035 $a 978-3-032-03989-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a TJFC $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .P825 2025
100 1 $a Popa, Cosmin Radu. $3 796417
245 1 0 $a Analog current-mode computational circuits for artificial neural networks $h [electronic resource] / $c by Cosmin Radu Popa.
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xxiii, 397 p. : $b ill., digital ; $c 24 cm.
490 1 $a Analog circuits and signal processing, $x 2197-1854
505 0 $a Introduction -- Superior-order approximation functions for generating sigmoidal activation functions -- Superior-order approximation functions for generating radial basis activation functions -- Superior-order approximation functions for artificial neural networks applications -- Analysis and design of analog function synthesizers for implmenting sigmoidal activation functions -- Analysis and design of analog function synthesizers for generating radial basis activation functions -- Analysis and design of analog function synthesizers for artificial neural networks applications -- Low-voltage low-power current-mode CMOS computational circuits for implementing activation functions -- Conclusions.
520 $a This book discusses in detail low-voltage low-power designs for minimizing the hardware resources required by neural network implementations. The novel method presented in this book for an accurate realization of activation functions for artificial neural networks (ANNs), is based on specific superior-order approximation functions. The author describes analog implementations in CMOS technology to increase the speed of operation, while reducing the hardware resources required for obtaining these approximation functions. Original architectures presented in this book, used for implementing previous CMOS computational structures, allow for operation independent of technological errors and temperature variations. SPICE simulations confirm the theoretically estimated results for previously presented CMOS computational structures, developed for ANNs and artificial intelligence applications.
650 0 $a Neural networks (Computer science) $3 528588
650 0 $a Analog integrated circuits. $3 1107847
650 1 4 $a Electronics Design and Verification. $3 1387809
650 2 4 $a Embedded Systems. $3 1026431
650 2 4 $a Cyber-Physical Systems. $3 1387591
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Analog circuits and signal processing. $3 885481
856 4 0 $u https://doi.org/10.1007/978-3-032-03989-7
950 $a Engineering (SpringerNature-11647)