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Full-Text Articles in Engineering
Modeling And Experimental Demonstration Of A Hopfield Network Analog-To-Digital Converter With Hybrid Cmos/Memristor Circuits, Xinjie Guo, Farnood Merrikh-Bayat, Ligang Gao, Brian D. Hoskins, Fabien Alibart, Bernabe Linares-Barranco, Luke Theogarajan, Christof Teuscher, Dmitri B. Strukov
Modeling And Experimental Demonstration Of A Hopfield Network Analog-To-Digital Converter With Hybrid Cmos/Memristor Circuits, Xinjie Guo, Farnood Merrikh-Bayat, Ligang Gao, Brian D. Hoskins, Fabien Alibart, Bernabe Linares-Barranco, Luke Theogarajan, Christof Teuscher, Dmitri B. Strukov
Electrical and Computer Engineering Faculty Publications and Presentations
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− …
Hierarchical Composition Of Memristive Networks For Real-Time Computing, Jens Bürger, Alireza Goudarzi, Darko Stefanovic, Christof Teuscher
Hierarchical Composition Of Memristive Networks For Real-Time Computing, Jens Bürger, Alireza Goudarzi, Darko Stefanovic, Christof Teuscher
Electrical and Computer Engineering Faculty Publications and Presentations
Advances in materials science have led to physical instantiations of self-assembled networks of memristive devices and demonstrations of their computational capability through reservoir computing. Reservoir computing is an approach that takes advantage of collective system dynamics for real-time computing. A dynamical system, called a reservoir, is excited with a time-varying signal and observations of its states are used to reconstruct a desired output signal. However, such a monolithic assembly limits the computational power due to signal interdependency and the resulting correlated readouts. Here, we introduce an approach that hierarchically composes a set of interconnected memristive networks into a larger reservoir. …