International Business Machines Corporation

Charles Mackin

Pritish Narayanan

Geoffrey W Burr

Stefano Ambrogio

Wilfried Haensch

Tayfun Gokmen

HsinYu Tsai

A system includes a processor, and a resistive processing resistive processing unit coupled to the processor. The resistive processing unit includes an array of cells, wherein the cells respectively include resistive devices, wherein at least a portion of the resistive devices are programmable to store weight values of a given matrix in the array of cells. When the given matrix is stored in the array of cells, the processor is configured to perform a weight extraction process. The weight extraction process applies a set of input vectors to the resistive processing unit to perform analog matrix-vector multiplication operations on the stored matrix, obtains a set of output vectors resulting from the analog matrix-vector multiplication operations, and determines weight values of the given matrix stored in the array of cells utilizing the set of input vectors and the set of output vectors.

Extraction of weight values in resistive processing unit array

A system can compensate for activation drift in analog memory-based artificial neural networks. A set of input activation vectors can be input, at a first point in time, to a crossbar array. The first set of output activation vectors can be read from the output lines of the crossbar array. At a second point in time, which is a later time than the first point in time, the input set of activation vectors can be input to the crossbar array. A second set of output activation vectors can be read from the crossbar array. A function that maps the second set of output activation vectors to the first set of output activation vectors can be determined. The function can be applied to subsequent output activation vectors output by the crossbar array. A method thereof, can also be provided.

Compensation for conductance drift in analog memory in crossbar array

A circuit comprises a first pulse-width modulator configured to generate a first pulse based on a first input, a second pulse-width modulator configured to generate a second pulse based on a second input, a first differential circuit comprising a first transistor, a second transistor, a first resistor, and a second resistor, and a second differential circuit comprising a first transistor, a second transistor, a first resistor, and a second resistor. A gate of the first transistor of the first differential circuit and a gate of the second transistor of the first differential circuit, and a gate of the first transistor of the second differential circuit and a gate of the second transistor of the second differential circuit are configured to be controlled by the first and second pulse width modulators based on the first input and the second input.

Analog memory-based complex multiply-accumulate (MACC) compute engine

Distributing multiply-accumulate currents across segment mirrors by providing a circuit including an array of resistive elements, the array including rows and columns and first stage current mirrors, each of the first stage current mirrors being electrically coupled to a segment, wherein the segment comprises a columnar subset of the resistive elements, providing, by the array, a vector of current outputs equal to an analog vector-matrix product between a vector of voltage inputs to the array and a matrix of analog resistive weights within the array, wherein the voltage inputs encode a vector of analog input values, wherein each row of resistive elements corresponds to a specific voltage input, determining a score for each of the rows, determining a ranking of the rows of the array according to the score of each row, and mapping each row to a segment according to the ranking.

Distributing device array currents across segment mirrors

A computer implemented method includes updating weight values associated with a plurality of analog synapses in a cross-bar array that implements an artificial neural network by sending a pulse sequence to the analog synapses. Each analog synapse includes a conductance unit, wherein a weight value of the analog synapse is based on a conductance value of the conductance unit. The pulse sequence changes the conductance value. The method further includes comparing the weight values of the analog synapses with target weight values associated with the analog synapses and selecting a set of analog synapses based on the comparison. The method further includes updating the weight values of the selected analog synapses by sending a set of electric pulses of varying durations.

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Charles Mackin