Analog Current-Mode Computational Circuits for Artificial Neural Networks
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 superi...
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| Format: | Elektronički e-knjiga |
| Jezik: | engleski |
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Springer Nature Switzerland : Imprint: Springer,
2025.
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| Izdanje: | 1st ed. 2025. |
| Serija: | Analog Circuits and Signal Processing,
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| Teme: | |
| Online pristup: | https://doi.org/10.1007/978-3-032-03989-7 |
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| 100 | 1 | |a Popa, Cosmin Radu. |e author. |0 (orcid)0000-0002-2872-1331 |1 https://orcid.org/0000-0002-2872-1331 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Analog Current-Mode Computational Circuits for Artificial Neural Networks |h [electronic resource] / |c by Cosmin Radu Popa. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XXIII, 397 p. 386 illus.). |b 385 illus. in color. | ||
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| 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
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