Dr. Christof Teuscher has been selected as the Maseeh Professor of Electrical and Computer Engineering in the Maseeh College of Engineering and Computer Science at Portland State University. His visionary research seeks to redefine how we build computers and what we understand computing to be. Continue…
Check out our latest publication: Macias Nicholas Jesse, Teuscher Christof, Durbeck Lisa J. K, Design of Introspective Circuits for Analysis of Cell-Level Dis-orientation in Self-Assembled Cellular Systems, Frontiers in Robotics and AI, 3(2), 2016, http://dx.doi.org/10.3389/frobt.2016.00002
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.
Andrew Boysen at UPENN is maintaining a more recent and updated branch of our “legacy” MATLAB RBN toolbox. His code is available on GitHub: https://github.com/CSFive/MATLAB_Kauffman_NK_Random_Boolean_Network
Check out our latest paper on the “Computational capacity and energy consumption of complex resistive switch networks.” Open access URL: http://dx.doi.org/10.3934/matersci.2015.4.530
The results are relevant for the design and fabrication of novel computing architectures that harness random assemblies of emerging nanodevices.