Tag Archives: memristor

NEW PAPER: Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks

Lilak S, Woods W, Scharnhorst K, Dunham C, Teuscher C, Stieg AZ and Gimzewski JK (2021) Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks. Frontiers in Nanotechnology, 3:675792. doi: 10.3389/fnano.2021.675792

Abstract: Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing. This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.

New Paper: Impact of Memristor Defects in a Neuromorphic Radionuclide Identification System

NEW PAPER: J. I. Canales-Verdial, W. Woods, C. Teuscher, M. Osinski and P. Zarkesh-Ha, Impact of Memristor Defects in a Neuromorphic Radionuclide Identification System, Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, 2020, pp. 1-5, doi: https://doi.org/10.1109/ISCAS45731.2020.9180669
 

Two New Reservoir Computing Papers Published

N. Babson and C. Teuscher, Reservoir Computing with Complex Cellular Automata, Complex Systems, 28(4), 2019 pp. 433–455.
https://doi.org/10.25088/ComplexSystems.28.4.433

S. J. D. Tran and C. Teuscher, “Hierarchical Memcapacitive Reservoir Computing Architecture,” 2019 IEEE International Conference on Rebooting Computing (ICRC), San Mateo, CA, USA, 2019, pp. 1-6. https://doi.org/10.1109/ICRC.2019.8914716

A Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits

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.