TRAINING DNN BY UPDATING AN ARRAY USING A CHOPPER

    公开(公告)号:US20220327375A1

    公开(公告)日:2022-10-13

    申请号:US17226416

    申请日:2021-04-09

    Inventor: Tayfun Gokmen

    Abstract: Embodiments disclosed herein include a method of training a DNN. A processor initializes an element of an A matrix. The element may include a resistive processing unit. A processor determines incremental weight updates by updating the element with activation values and error values from a weight matrix multiplied by a chopper value. A processor reads an update voltage from the element. A processor determines a chopper product by multiplying the update voltage by the chopper value. A processor stores an element of a hidden matrix. The element of the hidden matrix may include a summation of continuous iterations of the chopper product. A processor updates a corresponding element of a weight matrix based on the element of the hidden matrix reaching a threshold state.

    WEIGHT REPETITION ON RPU CROSSBAR ARRAYS

    公开(公告)号:US20220138579A1

    公开(公告)日:2022-05-05

    申请号:US17086856

    申请日:2020-11-02

    Abstract: A method is presented for artificial neural network training. The method includes storing weight values in an array of resistive processing unit (RPU) devices, wherein the array of RPU devices represents a weight matrix, defining the weight matrix to have an output dimension that is smaller than the input dimension such that the weight matrix has a rectangular configuration, and converting the weight matrix from a rectangular configuration to a more square-shaped configuration by repeating or concatenating the rectangular configuration of the weight matrix to increase a signal strength of a backward pass signal by copying an input of repeated weight elements during a forward cycle pass, summing output computations from the repeated weight elements, updating each of the repeated weight elements according to a backpropagated error or alternatively updating only one of the repeated weight elements by setting all forward values except one to zero during an update pass.

    Auto Weight Scaling for RPUs
    6.
    发明申请

    公开(公告)号:US20200380349A1

    公开(公告)日:2020-12-03

    申请号:US16427598

    申请日:2019-05-31

    Abstract: Techniques for auto weight scaling a bounded weight range of RPU devices with the size of the array during ANN training are provided. In one aspect, a method of ANN training includes: initializing weight values winit in the array to a random value, wherein the array represents a weight matrix W with m rows and n columns; calculating a scaling factor β based on a size of the weight matrix W; providing digital inputs x to the array; dividing the digital inputs x by a noise and bound management factor α to obtain adjusted digital inputs x′; performing a matrix-vector multiplication of the adjusted digital inputs x′ with the array to obtain digital outputs y′; multiplying the digital outputs y′ by the noise and bound management factor α; and multiplying the digital outputs y′ by the scaling factor β to provide digital outputs y of the array.

    Noise and Signal Management for RPU Array
    7.
    发明申请

    公开(公告)号:US20200380348A1

    公开(公告)日:2020-12-03

    申请号:US16427559

    申请日:2019-05-31

    Abstract: Advanced noise and signal management techniques for RPU arrays during ANN training are provided. In one aspect of the invention, a method for ANN training includes: providing an array of RPU devices with pre-normalizers and post-normalizers; computing and pre-normalizing a mean and standard deviation of all elements of an input vector x to the array that belong to the set group of each of the pre-normalizers; and computing and post-normalizing the mean p and the standard deviation a of all elements of an output vector y that belong to the set group of each of the post-normalizers.

    Real time simulation monitoring
    9.
    发明授权

    公开(公告)号:US10693736B2

    公开(公告)日:2020-06-23

    申请号:US14515563

    申请日:2014-10-16

    Abstract: A method for monitoring at least one simulation program includes capturing, by a computer, a plurality of simulation data from the at least one simulation program, the capturing is performed in real time while the at least one simulation program is continuously streaming the plurality of simulation data, analyzing, by the computer, the captured plurality of simulation data using a streaming data software, identifying a plurality of predefined criteria within the analyzed plurality of simulation data, the plurality of predefined criteria includes at least one of an event, a result and a variable, and providing feedback to the at least one simulation program to modify a plurality of simulation parameters according to the at least one identified event, result and variable.

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