This work was supported by NSF grant CCF-1028378 and by the Air Force Office of Scientific Research (AFOSR) under MURI grant FA9550-12-1-0038, and by Spanish grant TEC2012-37868-C04-01(BIOSENSE) (with support from the European Regional Development Fund).
Frontiers in Neuroscience
Hopfield networks, Neural networks (Computer science), Electric circuits, Hybrid circuits, Memristors
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.
Guo, X., Merrikh-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.