Check out some new and cool work we’ve been involved in that was just published in the Frontiers in Neuroscience.
Citation: Xinjie Guo, Farnood Merrikh-Bayat, Ligang Gao, Fabien Alibart, Brian Hoskins, Luke Theogarajan, Christof Teuscher, Bernabe Linares-Barranco, Dmitri Strukov, Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits, Frontiers in Neuroscience, 9(00488), 2015. DOI: http://dx.doi.org/10.3389/fnins.2015.00488
Abstract: 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.