Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits
In: Frontiers in neuroscience, vol 9, iss DEC Frontiers in Neuroscience, Vol 9 (2015) Digital.CSIC. Repositorio Institucional del CSIC instname idUS. Depósito de Investigación de la Universidad de Sevilla Frontiers in Neuroscience; (2015)
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Zugriff:
The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2−x/Pt memristors and CMOS integrated circuit components.
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Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits
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Autor/in / Beteiligte Person: | Alibart, Fabien ; Gao, Ligang ; Guo, Xinjie ; Theogarajan, Luke ; Merrikh-Bayat, Farnood ; Teuscher, Christof ; Linares-Barranco, Bernabe ; Hoskins, Brian D. ; Strukov, Dmitri B. ; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
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Quelle: | Frontiers in neuroscience, vol 9, iss DEC Frontiers in Neuroscience, Vol 9 (2015) Digital.CSIC. Repositorio Institucional del CSIC instname idUS. Depósito de Investigación de la Universidad de Sevilla Frontiers in Neuroscience; (2015) |
Veröffentlichung: | eScholarship, University of California, 2015 |
Medientyp: | unknown |
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