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    PERFORMING XNOR EQUIVALENT OPERATIONS BY ADJUSTING COLUMN THRESHOLDS OF A COMPUTE-IN-MEMORY ARRAY
    14.
    发明申请
    PERFORMING XNOR EQUIVALENT OPERATIONS BY ADJUSTING COLUMN THRESHOLDS OF A COMPUTE-IN-MEMORY ARRAY 有权

    公开(公告)号：US20210073619A1

    公开(公告)日：2021-03-11

    申请号：US16565308

    申请日：2019-09-09

    Applicant: QUALCOMM Incorporated

    Inventor： Zhongze WANG ,  Edward TEAGUE ,  Max WELLING

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A method performs XNOR-equivalent operations by adjusting column thresholds of a compute-in-memory array of an artificial neural network. The method includes adjusting an activation threshold generated for each column of the compute-in-memory array based on a function of a weight value and an activation value. The method also includes calculating a conversion bias current reference based on an input value from an input vector to the compute-in-memory array, the compute-in-memory array being programmed with a set of weights. The adjusted activation threshold and the conversion bias current reference are used as a threshold for determining the output values of the compute-in-memory array.

    VARIANCE PROPAGATION FOR QUANTIZATION
    15.
    发明申请
    VARIANCE PROPAGATION FOR QUANTIZATION 审中-公开

    公开(公告)号：US20190354865A1

    公开(公告)日：2019-11-21

    申请号：US16417430

    申请日：2019-05-20

    Applicant: QUALCOMM Incorporated

    Inventor： Matthias REISSER ,  Max WELLING ,  Efstratios GAVVES ,  Christos LOUIZOS

    IPC: G06N3/08 ,  G06K9/62

    Abstract: A neural network may be configured to receive, during a training phase of the neural network, a first input at an input layer of the neural network. The neural network may determine, during the training phase, a first classification at an output layer of the neural network based on the first input. The neural network may adjust, during the training phase and based on a comparison between the determined first classification and an expected classification of the first input, weights for artificial neurons of the neural network based on a loss function. The neural network may output, during an operational phase of the neural network, a second classification determined based on a second input, the second classification being determined by processing the second input through the artificial neurons using the adjusted weights.

    SIGMA-DELTA POSITION DERIVATIVE NETWORKS
    16.
    发明申请
    SIGMA-DELTA POSITION DERIVATIVE NETWORKS 审中-公开

    公开(公告)号：US20180336469A1

    公开(公告)日：2018-11-22

    申请号：US15705161

    申请日：2017-09-14

    Applicant: QUALCOMM Incorporated

    Inventor： Peter O'CONNOR ,  Max WELLING

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/084 ,  G06N3/049 ,  G06N3/063

    Abstract: A method for processing temporally redundant data in an artificial neural network (ANN) includes encoding an input signal, received at an initial layer of the ANN, into an encoded signal. The encoded signal comprises the input signal and a rate of change of the input signal. The method also includes quantizing the encoded signal into integer values and computing an activation signal of a neuron in a next layer of the ANN based on the quantized encoded signal. The method further includes computing an activation signal of a neuron at each layer subsequent to the next layer to compute a full forward pass of the ANN. The method also includes back propagating approximated gradients and updating parameters of the ANN based on an approximate derivative of a loss with respect to the activation signal.

    PROBABILISTIC NUMERIC CONVOLUTIONAL NEURAL NETWORKS
    17.
    发明申请
    PROBABILISTIC NUMERIC CONVOLUTIONAL NEURAL NETWORKS 有权

    公开(公告)号：US20220108173A1

    公开(公告)日：2022-04-07

    申请号：US17491351

    申请日：2021-09-30

    Applicant: QUALCOMM Incorporated

    Inventor： Marc Anton FINZI ,  Roberto BONDESAN ,  Max WELLING

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Certain aspects of the present disclosure provide techniques for performing operations with probabilistic numeric convolutional neural network, including: defining a Gaussian Process based on a mean and a covariance of input data; applying a linear operator to the Gaussian Process to generate pre-activation data; applying a nonlinear operation to the pre-activation data to form activation data; and applying a pooling operation to the activation data to generate an inference.

    QUANTUM DEFORMED BINARY NEURAL NETWORKS
    18.
    发明申请
    QUANTUM DEFORMED BINARY NEURAL NETWORKS 有权

    公开(公告)号：US20220108154A1

    公开(公告)日：2022-04-07

    申请号：US17491426

    申请日：2021-09-30

    Applicant: QUALCOMM Incorporated

    Inventor： Roberto BONDESAN ,  Max WELLING

    IPC: G06N3/04 ,  G06N10/00 ,  G06N3/08 ,  G06K9/62 ,  G06K9/00

    Abstract: Certain aspects of the present disclosure provide techniques for processing data in a quantum deformed binary neural network, including: determining an input state for a layer of the quantum deformed binary neural network; computing a mean and variance for one or more observables in the layer; and returning an output activation probability based on the mean and variance for the one or more observables in the layer.

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