Patents by Inventor NANDAKUMAR SASIDHARAN RAJALEKSHMI
NANDAKUMAR SASIDHARAN RAJALEKSHMI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11907380Abstract: In an approach, a process stores a matrix of multibit values for a computation in an analog multiply-accumulate unit including at least one crossbar array of binary analog memory cells connected between respective pairs of word- and bit-lines of the array, where: bits of each multibit value are stored in cells connected along a word-line, and corresponding bits of values in a column of the matrix are stored in cells connected along a bit-line. In each of one or more computation stages for a cryptographic element, the process supplies a set of polynomial coefficients of an element bitwise to respective word-lines of the unit to obtain analog accumulation signals on the respective bit-lines. The process converts the analog signals to digital. The process processes the digital signals obtained from successive bits of the polynomial coefficients in each of the stages to obtain a computation result for the cryptographic element.Type: GrantFiled: May 17, 2021Date of Patent: February 20, 2024Assignee: International Business Machines CorporationInventors: Nandakumar Sasidharan Rajalekshmi, Flavio A. Bergamaschi, Evangelos Stavros Eleftheriou
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Publication number: 20230097217Abstract: Techniques are provided for learning static bound management parameters for an analog resistive processing unit system which is configured for neuromorphic computing. For example, a system comprises one or more processors which are configured to: perform a first training process to train a first artificial neural network model; perform a second training process to retrain the first artificial neural network model using matrix-vector compute operations which are a function of bound management parameters of an analog resistive processing unit system, to thereby generate a second artificial neural network model with learned static bound management parameters; and configure the resistive processing unit system to implement the second artificial neural network model and the learned static bound management parameters.Type: ApplicationFiled: September 25, 2021Publication date: March 30, 2023Inventors: Malte Johannes Rasch, Manuel Le Gallo-Bourdeau, HsinYu Tsai, Charles Mackin, Nandakumar Sasidharan Rajalekshmi, An Chen
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Publication number: 20220366059Abstract: In an approach, a process stores a matrix of multibit values for a computation in an analog multiply-accumulate unit including at least one crossbar array of binary analog memory cells connected between respective pairs of word- and bit-lines of the array, where: bits of each multibit value are stored in cells connected along a word-line, and corresponding bits of values in a column of the matrix are stored in cells connected along a bit-line. In each of one or more computation stages for a cryptographic element, the process supplies a set of polynomial coefficients of an element bitwise to respective word-lines of the unit to obtain analog accumulation signals on the respective bit-lines. The process converts the analog signals to digital. The process processes the digital signals obtained from successive bits of the polynomial coefficients in each of the stages to obtain a computation result for the cryptographic element.Type: ApplicationFiled: May 17, 2021Publication date: November 17, 2022Inventors: Nandakumar Sasidharan Rajalekshmi, Flavio A. Bergamaschi, Evangelos Stavros Eleftheriou
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Patent number: 11386319Abstract: Methods and apparatus are provided for training an artificial neural network, having a succession of neuron layers with interposed synaptic layers each storing a respective set of weights {w} for weighting signals propagated between its adjacent neuron layers, via an iterative cycle of signal propagation and weight-update calculation operations. Such a method includes, for at least one of the synaptic layers, providing a plurality Pl of arrays of memristive devices, each array storing the set of weights of that synaptic layer Sl in respective memristive devices, and, in a signal propagation operation, supplying respective subsets of the signals to be weighted by the synaptic layer Sl in parallel to the Pl arrays. The method also includes, in a weight-update calculation operation, calculating updates to respective weights stored in each of the Pl arrays in dependence on signals propagated by the neuron layers.Type: GrantFiled: March 14, 2019Date of Patent: July 12, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Manuel Le Gallo-Bourdeau, Nandakumar Sasidharan Rajalekshmi, Christophe Piveteau, Irem Boybat Kara, Abu Sebastian, Evangelos Stavros Eleftheriou
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Patent number: 11348002Abstract: Methods and apparatus are provided for training an artificial neural network having a succession of layers of neurons interposed with layers of synapses. A set of crossbar arrays of memristive devices, connected between row and column lines, implements the layers of synapses. Each memristive device stores a weight for a synapse interconnecting a respective pair of neurons in successive neuron layers. The training method includes performing forward propagation, backpropagation and weight-update operations of an iterative training scheme by applying input signals, associated with respective neurons, to row or column lines of the set of arrays to obtain output signals on the other of the row or column lines, and storing digital signal values corresponding to the input and output signals. The weight-update operation is performed by calculating digital weight-correction values for respective memristive devices, and applying programming signals to those devices to update the stored weights.Type: GrantFiled: June 29, 2018Date of Patent: May 31, 2022Assignee: International Business Machines CorporationInventors: Irem Boybat Kara, Evangelos Stavros Eleftheriou, Manuel Le Gallo-Bourdeau, Nandakumar Sasidharan Rajalekshmi, Abu Sebastian
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Patent number: 11188825Abstract: A computer-implemented method of mixed-precision deep learning with multi-memristive synapses may be provided. The method comprises representing, each synapse of an artificial neural network by a combination of a plurality of memristive devices, wherein each of the plurality of memristive devices of each of the synapses contributes to an overall synaptic weight with a related device significance, accumulating a weight gradient ?W for each synapse in a high-precision variable, and performing a weight update to one of the synapses using an arbitration scheme for selecting a respective memristive device, according to which a threshold value related to the high-precision variable for performing the weight update is set according to the device significance of the respective memristive device selected by the arbitration schema.Type: GrantFiled: January 9, 2019Date of Patent: November 30, 2021Assignee: International Business Machines CorporationInventors: Irem Boybat Kara, Manuel Le Gallo-Bourdeau, Nandakumar Sasidharan Rajalekshmi, Abu Sebastian, Evangelos Stavros Eleftheriou
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Publication number: 20200293855Abstract: Methods and apparatus are provided for training an artificial neural network, having a succession of neuron layers with interposed synaptic layers each storing a respective set of weights {w} for weighting signals propagated between its adjacent neuron layers, via an iterative cycle of signal propagation and weight-update calculation operations. Such a method includes, for at least one of the synaptic layers, providing a plurality P1 of arrays of memristive devices, each array storing the set of weights of that synaptic layer S1 in respective memristive devices, and, in a signal propagation operation, supplying respective subsets of the signals to be weighted by the synaptic layer S1 in parallel to the P1 arrays. The method also includes, in a weight-update calculation operation, calculating updates to respective weights stored in each of the P1 arrays in dependence on signals propagated by the neuron layers.Type: ApplicationFiled: March 14, 2019Publication date: September 17, 2020Inventors: Manuel Le Gallo-Bourdeau, Nandakumar Sasidharan Rajalekshmi, Christophe Piveteau, Irem Boybat Kara, Abu Sebastian, Evangelos Stavros Eleftheriou
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Publication number: 20200118001Abstract: A computer-implemented method of mixed-precision deep learning with multi-memristive synapses may be provided. The method comprises representing, each synapse of an artificial neural network by a combination of a plurality of memristive devices, wherein each of the plurality of memristive devices of each of the synapses contributes to an overall synaptic weight with a related device significance, accumulating a weight gradient ?W for each synapse in a high-precision variable, and performing a weight update to one of the synapses using an arbitration scheme for selecting a respective memristive device, according to which a threshold value related to the high-precision variable for performing the weight update is set according to the device significance of the respective memristive device selected by the arbitration schema.Type: ApplicationFiled: January 9, 2019Publication date: April 16, 2020Inventors: Irem Boybat Kara, Manuel Le Gallo-Bourdeau, Nandakumar Sasidharan Rajalekshmi, Abu Sebastian, Evangelos Stavros Eleftheriou
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Publication number: 20190122105Abstract: Methods and apparatus are provided for training an artificial neural network having a succession of layers of neurons interposed with layers of synapses. A set of crossbar arrays of memristive devices, connected between row and column lines, implements the layers of synapses. Each memristive device stores a weight for a synapse interconnecting a respective pair of neurons in successive neuron layers. The training method includes performing forward propagation, backpropagation and weight-update operations of an iterative training scheme by applying input signals, associated with respective neurons, to row or column lines of the set of arrays to obtain output signals on the other of the row or column lines, and storing digital signal values corresponding to the input and output signals. The weight-update operation is performed by calculating digital weight-correction values for respective memristive devices, and applying programming signals to those devices to update the stored weights.Type: ApplicationFiled: June 29, 2018Publication date: April 25, 2019Inventors: IREM BOYBAT KARA, EVANGELOS STAVROS ELEFTHERIOU, MANUEL LE GALLO-BOURDEAU, NANDAKUMAR SASIDHARAN RAJALEKSHMI, ABU SEBASTIAN