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    METHOD AND DEVICE FOR DETERMINING A GLOBAL MEMORY SIZE OF A GLOBAL MEMORY SIZE FOR A NEURAL NETWORK
    11.
    发明申请
    METHOD AND DEVICE FOR DETERMINING A GLOBAL MEMORY SIZE OF A GLOBAL MEMORY SIZE FOR A NEURAL NETWORK 审中-公开

    公开(公告)号：US20200302278A1

    公开(公告)日：2020-09-24

    申请号：US16810546

    申请日：2020-03-05

    Applicant: STMicroelectronics (Rousset) SAS

    Inventor： Laurent Folliot ,  Pierre Demaj

    IPC: G06N3/063 ,  G06F12/02

    Abstract: In accordance with an embodiment, a method for determining an overall memory size of a global memory area configured to store input data and output data of each layer of a neural network includes: for each current layer of the neural network after a first layer, determining a pair of elementary memory areas based on each preceding elementary memory area associated with a preceding layer, wherein: the two elementary memory areas of the pair of elementary memory areas respectively have two elementary memory sizes, each of the two elementary memory areas are configured to store input data and output data of the current layer of the neural network, the output data is respectively stored in two different locations, and the overall memory size of the global memory area corresponds to a smallest elementary memory size at an output of the last layer of the neural network.

    ARTIFICIAL NEURON NETWORK HAVING AT LEAST ONE UNIT CELL QUANTIFIED IN BINARY
    17.
    发明公开
    ARTIFICIAL NEURON NETWORK HAVING AT LEAST ONE UNIT CELL QUANTIFIED IN BINARY 审中-公开

    公开(公告)号：US20240095502A1

    公开(公告)日：2024-03-21

    申请号：US18470281

    申请日：2023-09-19

    Applicant: STMicroelectronics (Rousset) SAS

    Inventor： Pierre Demaj ,  Laurent Folliot

    IPC: G06N3/0464

    CPC classification number: G06N3/0464

    Abstract: An artificial neural network includes a unit cell. The unit cell includes a first binary two-dimensional convolution layer configured to receive an input tensor and to generate a first tensor. A first batch normalization layer is configured to receive the first tensor and to generate a second tensor. A concatenation layer is configured to generate a third tensor by concatenating the input tensor and the second tensor. A second binary two-dimensional convolution layer is configured to receive the third tensor and to generate a fourth tensor. A second batch normalization layer is configured to generate an output tensor based on the fourth tensor.

    METHOD FOR UPDATING AN ARTIFICIAL NEURAL NETWORK
    18.
    发明申请
    METHOD FOR UPDATING AN ARTIFICIAL NEURAL NETWORK 有权

    公开(公告)号：US20220164664A1

    公开(公告)日：2022-05-26

    申请号：US17510273

    申请日：2021-10-25

    Applicant: STMicroelectronics (Rousset) SAS

    Inventor： Pierre Demaj ,  Laurent Folliot

    IPC: G06N3/08 ,  G06F7/499 ,  G06F7/02

    Abstract: According to one aspect, the disclosure proposes a method for updating an artificial neural network including initial weights stored in a memory at least in an integer format, which method includes: a processing unit determining the error gradients at the output of the layers of the neural network, the processing unit retrieving the initial weights from memory, the processing unit updating the initial weights comprising, for each initial weight, a first calculation of a corrected weight, in the integer format of this initial weight, the processing unit replacing the value of the initial weights stored in the memory by the value of the corrected weights.

    METHOD AND DEVICE FOR PROCESSING DATA THROUGH A NEURAL NETWORK
    20.
    发明申请
    METHOD AND DEVICE FOR PROCESSING DATA THROUGH A NEURAL NETWORK 审中-公开

    公开(公告)号：US20200184331A1

    公开(公告)日：2020-06-11

    申请号：US16691914

    申请日：2019-11-22

    Applicant: STMicroelectronics (Rousset) SAS

    Inventor： Pierre Demaj ,  Laurent Folliot

    IPC: G06N3/08 ,  G06N20/00 ,  G06F17/16

    Abstract: A method can be used to process an initial set of data through a convolutional neural network that includes a convolution layer followed by a pooling layer. The initial set is stored in an initial memory along first and second orthogonal directions. The method includes performing a first filtering of the initial set of data by the convolution layer using a first sliding window along the first direction. Each slide of the first window produces a first set of data. The method also includes performing a second filtering of the first sets of data by the pooling layer using a second sliding window along the second direction.

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