Project Details
Projekt
REservoir COMputing with MEmristive Nonlinear Dynamics: Theory, Design and Applications (RECOMMEND)
Applicants
Professor Fernando Corinto, Ph.D.; Professor Dr. Ronald Tetzlaff; Professor Dr. Martin Ziegler
Subject Area
Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 536063366
Reservoir computing (RC) is an efficient machine learning method for temporal/sequential data processing. RC uses a nonlinear representation of the input data in a high-dimensional space called a reservoir. Recently, this method has been shown to have similarities to statistical methods such as nonlinear vector autoregression, which does not require a high-dimensional reservoir and therefore can be implemented with a smaller number of components and produces interpretable results. The Next Generation Reservoir Computing (NGRC) concept combines these approaches and promises extremely efficient neuromorphic circuit implementations. The goal of the REservoir COMputing with MEmristor Nonlinear Dynamics: Theory, Design and Applications (RECOMMEND) project is to realize a reconfigurable, energy-efficient neuromorphic computing system at the hardware and modeling levels by using theoretical foundations to develop a hardware prototype of a versatile RC platform based on 1) a low-dimensional nonlinear dynamic reservoir circuit with memristors as nonlinear tunable dynamic elements. 2) computational/memory elements that enable one-step in-memory vector-matrix multiplication (VMM). 3) supervised control circuitry that enables multitasking and correct functionality under time-varying conditions. Hardware versions of memristors that exhibit electrically induced resistance change, along with scalable electronic components (resistors, capacitors), will form the analog core of the RC platform. Using circuit and system theory principles (flux charge analysis and Volterra methods), the resulting tunable nonlinear dynamics of the devices will be used to map the input data into an appropriate vector of nonlinear elements to control the output layer of the network for time series prediction and optimization problem solving. Research is conducted at the intersection of several complementary technical fields, including materials science, device physics, and circuit and system theoretical modeling, including analytical methods to support circuit design, analysis of nonlinear dynamic characteristics of nonautonomous circuits, to the development of mathematical tools and algorithms. The goal of our multidisciplinary consortium is thus to enable a radical paradigm shift in information processing towards energy-efficient, robust, and scalable RC platforms with memristors that bridge the gap between statistical methods and machine learning.
DFG Programme
Priority Programmes
International Connection
Italy
