METHOD FOR ANALYZING A SET OF PARAMETERS OF A NEURAL NETWORK

    公开(公告)号:US20200175373A1

    公开(公告)日:2020-06-04

    申请号:US16680840

    申请日:2019-11-12

    Abstract: A method can be used with a neural network being implemented by a system having a computation unit coupled to a collection of memories. The method includes analyzing a set of initial parameters defining an initial multilayer neural network. The analyzing includes attempting to reduce an initial memory size of an initial parameter so as to obtain a set of modified parameters defining a modified neural network with respect to the initial network.

    METHOD FOR GENERATING COMPUTER-EXECUTABLE CODE FOR IMPLEMENTING AN ARTIFICIAL NEURAL NETWORK

    公开(公告)号:US20240119309A1

    公开(公告)日:2024-04-11

    申请号:US18470798

    申请日:2023-09-20

    CPC classification number: G06N3/10 G06F8/35 G06F8/4434

    Abstract: In an embodiments a method includes obtaining a neural network (INN), the neural network having a plurality of neural layers, each layer being capable of being executed according to different implementation solutions and impacting a required memory allocation for the execution of the neural network and/or an execution time of the neural network, defining a maximum execution time threshold of the neural network and/or a maximum required memory allocation threshold for the execution of the neural network, determining an optimal required memory allocation size for the execution of the neural network from possible implementation solutions for each layer of the neural network, determining an optimal execution time of the neural network from the possible implementation solutions for each layer of the neural network and estimating a performance loss or a performance gain in terms of execution time and required memory allocation for each implementation solution of each layer of the neural network.

    METHOD FOR MANAGING A CONVOLUTIONAL COMPUTATION AND CORRESPONDING DEVICE

    公开(公告)号:US20220107990A1

    公开(公告)日:2022-04-07

    申请号:US17480639

    申请日:2021-09-21

    Abstract: In an embodiment a method for managing a convolutional calculation carried out by a calculation unit adapted to calculate output data on output channels from convolution kernels applied to input data blocks on at least one input channel, wherein calculations on each input data block correspond respectively to an output datum on an output channel, and wherein the calculations with each convolution kernel correspond to the output data on each output channel respectively includes identifying a size of a memory location available in a temporary working memory of the calculation unit, pre-loading in the temporary working memory a maximum number of convolution kernels storable at the size of the memory; and controlling the calculation unit to calculate a set of output data calculable from pre-loaded convolution kernels.

    Method and apparatus for real-time detection of a scene

    公开(公告)号:US10789477B2

    公开(公告)日:2020-09-29

    申请号:US15936587

    申请日:2018-03-27

    Abstract: A method for real-time detection of at least one scene by an apparatus, from among a set of possible reference scenes, includes acquiring current values of attributes from measurement values supplied by sensors. The method further includes traversing a path through a decision tree. The nodes of the decision tree are respectively associated with the attributes. The traversal considers at each node along the path, the current value of the corresponding attribute, so as to obtain at the output of the path, a scene from among the set of reference scenes. The obtained scene identifying which reference scene is the detected scene. The method further includes developing a confidence index (SC) associated with the identification of the detected scene.

    METHOD AND APPARATUS FOR REAL-TIME DETECTION OF A SCENE

    公开(公告)号:US20180293441A1

    公开(公告)日:2018-10-11

    申请号:US15936587

    申请日:2018-03-27

    Abstract: A method for real-time detection of at least one scene by an apparatus, from among a set of possible reference scenes, includes acquiring current values of attributes from measurement values supplied by sensors. The method further includes traversing a path through a decision tree. The nodes of the decision tree are respectively associated with the attributes. The traversal considers at each node along the path, the current value of the corresponding attribute, so as to obtain at the output of the path, a scene from among the set of reference scenes. The obtained scene identifying which reference scene is the detected scene. The method further includes developing a confidence index (SC) associated with the identification of the detected scene.

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