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    Indexing Elements in a Source Array
    2.
    发明申请
    Indexing Elements in a Source Array 有权

    公开(公告)号：US20220012222A1

    公开(公告)日：2022-01-13

    申请号：US17321091

    申请日：2021-05-14

    Applicant: Imagination Technologies Limited

    Inventor： Aria Ahmadi ,  Cagatay Dikici

    IPC: G06F16/22 ,  G06F17/11

    Abstract: A hardware-implemented method of indexing data elements in a source array is provided. The method comprises generating a number of shifted copy arrays; receiving indices for indexing the source array; and retrieving one or more data elements from the shifted copy arrays, according to the received indices. Also disclosed is a related processing system comprising a memory and hardware for indexing data elements in a source array in the memory.

    IMPLEMENTING A SCATTER FUNCTION ON A NEURAL NETWORK ACCELERATOR
    3.
    发明公开
    IMPLEMENTING A SCATTER FUNCTION ON A NEURAL NETWORK ACCELERATOR 审中-公开

    公开(公告)号：US20240169025A1

    公开(公告)日：2024-05-23

    申请号：US18385690

    申请日：2023-10-31

    Applicant: Imagination Technologies Limited

    Inventor： Le Yang ,  Aria Ahmadi ,  Cagatay Dikici

    IPC: G06F17/16

    CPC classification number: G06F17/16

    Abstract: A method of implementing a scatter operation in fixed-function hardware of a neural network accelerator involves converting two or more vectors of indices to sparse index tensors in a one-hot sparse format. An update tensor is generated, by applying the update values to one of the sparse index tensors (or a tensor derived from it). In some examples, an input data tensor is updated based on the update tensor. In other examples, the update tensor itself is output.

    METHODS AND SYSTEMS FOR EXECUTING A NEURAL NETWORK ON A NEURAL NETWORK ACCELERATOR
    7.
    发明公开
    METHODS AND SYSTEMS FOR EXECUTING A NEURAL NETWORK ON A NEURAL NETWORK ACCELERATOR 审中-公开

    公开(公告)号：US20240143986A1

    公开(公告)日：2024-05-02

    申请号：US18216008

    申请日：2023-06-29

    Applicant: Imagination Technologies Limited

    Inventor： Aria Ahmadi ,  Cagatay Dikici ,  Clement Charnay ,  Jason Rogers

    IPC: G06N3/063 ,  G06N3/0464

    CPC classification number: G06N3/063 ,  G06N3/0464

    Abstract: Methods of dividing a neural network into chunks of operations executable in a hardware pass of hardware to execute a neural network. The layers of the neural network are divisible into layer groups that comprise a sequence of layers executable in the same hardware pass of the hardware. Each layer group is divisible into chunks of operations executable in a hardware pass of the hardware. The chunks for a layer group are defined by split parameters. A layer group loss function is obtained that represents a performance metric associated with executing a layer group on the hardware as a function of the split parameters and neural network architecture parameters for the layer group. A neural network loss function is generated based on the layer group loss function that represents the performance metric associated with executing the neural network on the hardware; and the split parameters for the one or more layer groups are selected that minimize the neural network loss function under constraints imposed by the hardware.

    METHODS AND SYSTEMS FOR GENERATING THE GRADIENTS OF A LOSS FUNCTION WITH RESPECT TO THE WEIGHTS OF A CONVOLUTION LAYER
    8.
    发明申请
    METHODS AND SYSTEMS FOR GENERATING THE GRADIENTS OF A LOSS FUNCTION WITH RESPECT TO THE WEIGHTS OF A CONVOLUTION LAYER 有权

    公开(公告)号：US20220351036A1

    公开(公告)日：2022-11-03

    申请号：US17699090

    申请日：2022-03-19

    Applicant: Imagination Technologies Limited

    Inventor： Aria Ahmadi ,  Cagatay Dikici

    IPC: G06N3/08

    Abstract: Methods and systems of generating gradients of a loss metric for a neural network (NN) with respect to weights of a convolution layer of the NN, the convolution layer of the NN configured to receive an input tensor of input values and a weight tensor of weights, and generate an output tensor of output values. The methods comprise performing, using hardware logic, a group convolution between a first y-dimensional tensor and a second z-dimensional tensor wherein z=y+1, the first y-dimensional tensor being formed of a set of values from the input tensor, and the second z-dimensional tensor being formed of a set of values from an output gradient tensor comprising gradients of the loss metric for the NN with respect to the output values; wherein the first y-dimensional tensor, the second z-dimensional tensor and the group convolutions are configured to generate an output of a convolution operation between each channel of the set of values of the input tensor and each channel of the set of values of the output gradient tensor.

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