Emergence of complex behavior in Memristor Cellular Nonlinear Networks (ECOM)

• Applicant: Professor Dr. Ronald Tetzlaff
• Subject Area: Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
• Term: from 2017 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 379950170

The aim of this project is to gain a deep insight into the computational capabilities of memristor Cel-lular Nonlinear/Nanoscale Networks (MCNN) in view of the need to improve the performance of state-of-the art sensorprocessor arrays, which, despite operating at frame rates higher than 20 kHz, have a limited applicability sphere, due to the low resolution. Memristors are nano-scale elements with a rich nonlinear dynamical behavior and represent the most efficient emulators of neural synap-tic dynamics. There exist a number of distinct classes of memristors, each with specific properties. One of such classes include elements capable to compute as well as store data. Another class, very important for this project, is composed of elements, which may exhibit locally-active behavior, and thus induce complex dynamics in electronic circuits based upon them. Very interestingly, some memristors, manufactured using materials such Niobium oxide, belong to both of the mentioned classes, since memory switching and threshold switching with local activity may coexist in these devices. The use of memristors in CNN may lead to the extension of the resolution limits of state-of-the art sensor-processor arrays. In view of the promising perspectives of memristor-based CNNs, the proposed research project aims at deriving a robust theoretical framework on the circuit-theoretic properties as well as on the nonlinear dynamic behaviors of these novel arrays for the development of new forms of computation to improve the functionalities of current hardware solutions. Complex image processing problems may be solved by exploiting the formation of inhomogenous spatio-temporal patterns within the array. However, no static or dynamic pattern may arise in the network if the overall system is locally passive. As a result, the most significant goal of this research is to ex-tend the local activity theory so as to characterize the complex dynamics developing in memristor CNNs under the satisfaction of suitable local activity criteria. The analysis will be generalized so as to apply for a large class of memristor synapse models. The derivation of the parameter domain where a CNN cell acts as a locally-active system will be based on a rigorous mathematical treatment. All in all, the proposed research is of fundamental importance to gain a deeper insight into the com-putational functionalities of these novel bio-inspired networks, which may pave the way towards fu-ture computing machines with parallel processing power, size and energy consumption resembling the performance of the human brain, and resolution capabilities outperforming conventional arrays.

• DFG Programme: Research Grants
• International Connection: Bulgaria
• Cooperation Partner: Professorin Dr. Angela Slavova