LEADER 03604nam  22005655  450 
001 9911039319703321
005 20251107114906.0
010   $a9783032039897$b(electronic bk.)
010   $z9783032039880
024 7 $a10.1007/978-3-032-03989-7
035   $a(MiAaPQ)EBC32405110
035   $a(Au-PeEL)EBL32405110
035   $a(CKB)42032179700041
035   $a(DE-He213)978-3-032-03989-7
035   $a(EXLCZ)9942032179700041
100   $a20251107d2025      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAnalog Current-Mode Computational Circuits for Artificial Neural Networks /$fby Cosmin Radu Popa
205   $a1st ed. 2025.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025.
215   $a1 online resource (233 pages)
225 1 $aAnalog Circuits and Signal Processing,$x2197-1854
311 08$aPrint version: Popa, Cosmin Radu Analog Current-Mode Computational Circuits for Artificial Neural Networks Cham : Springer,c2025 9783032039880 
327   $aIntroduction -- 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.
330   $aThis book discusses in detail low-voltage low-power designs for minimizing the hardware resources required by neural network implementations. The novel method presented in this book for an accurate realization of activation functions for artificial neural networks (ANNs), is based on specific superior-order approximation functions. The author describes analog implementations in CMOS technology to increase the speed of operation, while reducing the hardware resources required for obtaining these approximation functions. Original architectures presented in this book, used for implementing previous CMOS computational structures, allow for operation independent of technological errors and temperature variations. SPICE simulations confirm the theoretically estimated results for previously presented CMOS computational structures, developed for ANNs and artificial intelligence applications.
410  0$aAnalog Circuits and Signal Processing,$x2197-1854
606   $aElectronic circuit design
606   $aEmbedded computer systems
606   $aCooperating objects (Computer systems)
606   $aElectronics Design and Verification
606   $aEmbedded Systems
606   $aCyber-Physical Systems
615  0$aElectronic circuit design.
615  0$aEmbedded computer systems.
615  0$aCooperating objects (Computer systems).
615 14$aElectronics Design and Verification.
615 24$aEmbedded Systems.
615 24$aCyber-Physical Systems.
676   $a621.3815
700   $aPopa$b Cosmin Radu$01061259
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9911039319703321
996   $aAnalog Current-Mode Computational Circuits for Artificial Neural Networks$94454516
997   $aUNINA