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公开(公告)号：US20230409869A1
公开(公告)日：2023-12-21
申请号：US18316152
申请日：2023-05-11
发明人： Laurent Folliot , Pierre Demaj
IPC分类号： G06N3/04
CPC分类号： G06N3/04
摘要： According to one aspect, there is proposed a method for transforming a trained artificial neural network including a binary convolution layer followed by a pooling layer then a batch normalization layer, the method includes obtaining the trained artificial neural network and transforming the trained artificial neural network such that the order of the layers of the trained artificial neural network is modified by displacing the batch normalization layer after the convolution layer.
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公开(公告)号：US11593664B2
公开(公告)日：2023-02-28
申请号：US16917414
申请日：2020-06-30
发明人： Laurent Folliot , Pierre Demaj , Emanuele Plebani
摘要： A method can be performed prior to implementation of a neural network by a processing unit. The neural network comprising a succession of layers and at least one operator applied between at least one pair of successive layers. A computational tool generates an executable code intended to be executed by the processing unit in order to implement the neural network. The computational tool generates at least one transfer function between the at least one pair of layers taking the form of a set of pre-computed values.
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3.发明授权公开(公告)号：US11500767B2
公开(公告)日：2022-11-15
申请号：US16810546
申请日：2020-03-05
发明人： Laurent Folliot , Pierre Demaj
摘要： 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.
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4.发明申请公开(公告)号：US20200302278A1
公开(公告)日：2020-09-24
申请号：US16810546
申请日：2020-03-05
发明人： Laurent Folliot , Pierre Demaj
摘要： 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.
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5.发明申请公开(公告)号：US20200169631A1
公开(公告)日：2020-05-28
申请号：US16774954
申请日：2020-01-28
发明人： Pierre Demaj , Laurent Folliot
IPC分类号： H04M1/725 , G01D21/02 , H04W4/02 , H04W12/00 , H04W4/029 , H04W52/02 , G06F9/445 , G06F3/01 , G06K9/00
摘要： A method of real-time scene detection performed by a wireless communication device includes, performing a first scene detection measurement to determine that the wireless communication device is located in a first scene. The first scene detection measurement is performed at first instant in time. The first scene is a type of environment. The method further includes associating the first scene with a corresponding reference scene of a predetermined set of reference scenes, determining a reference duration associated with the corresponding reference scene, and performing a second scene detection measurement immediately following expiration of the reference duration measured from the first instant in time.
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公开(公告)号：US20230131067A1
公开(公告)日：2023-04-27
申请号：US17968163
申请日：2022-10-18
发明人： Pierre Demaj , Laurent Folliot
摘要： According to one aspect, a method is proposed for detecting events or elements in physical signals by implementing an artificial neural network. The method includes an assessment of a probability of the presence of the event or the element by an implementation of the neural network. The implementation of the neural network according to a nominal mode takes as input a physical signal having a first resolution, called nominal resolution, when the probability of presence of the event or the element is greater than a threshold. The implementation of the neural network according to a low power mode takes as input a physical signal having a second resolution, called reduced resolution, lower than the first resolution, when the probability of presence of the event or the element is below the threshold.
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公开(公告)号：US11354238B2
公开(公告)日：2022-06-07
申请号：US16691957
申请日：2019-11-22
发明人： Laurent Folliot , Pierre Demaj
摘要： A method can be used to determine an overall memory size of a global memory area to be allocated in a memory intended to store input data and output data from each layer of a neural network. An elementary memory size of an elementary memory area intended to store the input data and the output data from the layer is determined for each layer. The elementary memory size is in the range between a memory size for the input data or output data from the layer and a size equal to the sum of the memory size for the input data and the memory size for the output data from the layer. The overall memory size is determined based on the elementary memory sizes associated with the layers. The global memory area contains all the elementary memory areas.
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公开(公告)号：US10863018B2
公开(公告)日：2020-12-08
申请号：US16774954
申请日：2020-01-28
发明人： Pierre Demaj , Laurent Folliot
IPC分类号： H04M1/725 , G01D21/02 , H04W4/02 , H04W12/00 , H04W4/029 , H04W52/02 , G06F9/445 , G06F3/01 , G06K9/00 , H04W4/38 , H04W56/00 , H04W4/33 , H04W4/23
摘要： A method of real-time scene detection performed by a wireless communication device includes, performing a first scene detection measurement to determine that the wireless communication device is located in a first scene. The first scene detection measurement is performed at first instant in time. The first scene is a type of environment. The method further includes associating the first scene with a corresponding reference scene of a predetermined set of reference scenes, determining a reference duration associated with the corresponding reference scene, and performing a second scene detection measurement immediately following expiration of the reference duration measured from the first instant in time.
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公开(公告)号：US20200302266A1
公开(公告)日：2020-09-24
申请号：US16810582
申请日：2020-03-05
发明人： Pierre Demaj , Laurent Folliot
IPC分类号： G06N3/04
摘要： In accordance with an embodiment, a method includes reducing a size of at least one initial parameter of each layer of an initial multilayer neural network to obtain for each layer a set of new parameters defining a new neural network, wherein each new parameter of the set of new parameters has its data represented in two portions comprising an integer portion and a fractional portion; implementing the new neural network using a test input data set applied only once to each layer; determining a distribution function or a density function resulting from the set of new parameters for each layer; and based on the determined distribution function or density function, adjusting a size of a memory area allocated to the fractional portion and a size of the memory area allocated to the integer portion of each new parameter associated with each layer.
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公开(公告)号：US20200183834A1
公开(公告)日：2020-06-11
申请号：US16691957
申请日：2019-11-22
发明人： Laurent Folliot , Pierre Demaj
摘要： A method can be used to determine an overall memory size of a global memory area to be allocated in a memory intended to store input data and output data from each layer of a neural network. An elementary memory size of an elementary memory area intended to store the input data and the output data from the layer is determined for each layer. The elementary memory size is in the range between a memory size for the input data or output data from the layer and a size equal to the sum of the memory size for the input data and the memory size for the output data from the layer. The overall memory size is determined based on the elementary memory sizes associated with the layers. The global memory area contains all the elementary memory areas.
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