Artículo
Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits
Autor/es | Guo, Xinjie
Merrihk-Bayat, F. Gao, Ligand Hoskins, Brian D. Alibart, Fabien Linares Barranco, Bernabé Theogarajan, Luke Teucher, Christof Strukov, Dmitri B. |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2015 |
Fecha de depósito | 2018-05-03 |
Publicado en |
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Resumen | 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 ... 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. |
Identificador del proyecto | CCF-1028378
FA9550-12-1-0038 TEC2012-37868-C04-01 |
Cita | Guo, X., Merrihk-Bayat, F., Gao, L., Hoskins, B.D., Alibart, F., Linares Barranco, B.,...,Strukov, D.B. (2015). Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits. Frontiers in Neuroscience, 9, 488-. |
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