Abstract: Method and apparatus for performing close-loop programming of resistive memory devices in crossbar array based hardware circuits and systems. Invention provides iterative training of memristor crossbar arrays for neural networks by applying voltages corresponding to selected training patterns. Error is detected and measured as a function of the actual response to the training patterns versus the expected response to the training pattern.

Abstract: Invention provides an apparatus and method for performing signal processing on a crossbar array of resistive memory devices. The invention is implemented using one or multiple crossbar arrays of resistive memory devices in conjunction with devices for converting input real number representations to voltage waveforms, devices for converting current waveforms into voltage waveforms, and devices for converting voltage waveforms to real numbers outputs.

Abstract: An apparatus that performs the mathematical matrix-vector multiplication approximation operations using crossbar arrays of resistive memory devices (e.g. memristor, resistive random-access memory, spintronics, etc.). A crossbar array formed by resistive memory devices serves as a memory array that stores the coefficients of a matrix. Combined with input and output analog circuits, the crossbar array system realizes the method of performing matrix-vector multiplication approximation operations with significant performance, area and energy advantages over existing methods and designs. This invention also includes an extended method that realizes the auto-associative neural network recall function using the resistive memory crossbar architecture.

Abstract: A method for context-aware text recognition employing two neuromorphic computing models, auto-associative neural network and cogent confabulation. The neural network model performs the character recognition from input image and produces one or more candidates for each character in the text image input. The confabulation models perform the context-aware text extraction and completion, based on the character recognition outputs and the word and sentence knowledge bases.

Abstract: Method and apparatus for performing close-loop programming of resistive memory devices in crossbar array based hardware circuits and systems. Invention provides iterative training of memristor crossbar arrays for neural networks by applying voltages corresponding to selected training patterns. Error is detected and measured as a function of the actual response to the training patterns versus the expected response to the training pattern.

Abstract: An apparatus that performs the mathematical matrix-vector multiplication approximation operations using crossbar arrays of resistive memory devices (e.g. memristor, resistive random-access memory, spintronics, etc.). A crossbar array formed by resistive memory devices serves as a memory array that stores the coefficients of a matrix. Combined with input and output analog circuits, the crossbar array system realizes the method of performing matrix-vector multiplication approximation operations with significant performance, area and energy advantages over existing methods and designs. This invention also includes an extended method that realizes the auto-associative neural network recall function using the resistive memory crossbar architecture.

Abstract: A method for context-aware text recognition employing two neuromorphic computing models, auto-associative neural network and cogent confabulation. The neural network model performs the character recognition from input image and produces one or more candidates for each character in the text image input. The confabulation models perform the context-aware text extraction and completion, based on the character recognition outputs and the word and sentence knowledge bases.