Stmicroelectronics (rousset) Sas

St-ericsson Sa

Stmicroelectronics S.r.l.

Telefonaktiebolaget Lm Ericsson (publ)

Stmicroelectronics S.a.

Optis Circuit Technology, LLC

Stmicroelectronics (grenoble 2) Sas

Pierre Demaj

Laurent Folliot

Fabrizio Tomatis

Yoann Bouvet

Stefania Sesia

Mark Wallis

Giuseppe Montalbano

Ahmet Bastug

Martial Gander

Muhammad Ali Kazmi

Andrea Ancora

Franz J Eder

Mirko Falchetto

Emanuele Plebani

Eric Alliot

Philippe Thierion

Matthieu Durnerin

Ludovic Champsaur

Guiseppe Montalbano

According to an aspect, a method is proposed for defining placements, in a volatile memory, of temporary scratch buffers used during an execution of an artificial neural network, the method comprising: determining an execution order of layers of the neural network, defining placements, in a heap memory zone of the volatile memory, of intermediate result buffers generated by each layer, according to the execution order of the layers, determining at least one free area of the heap memory zone over the execution of the layers, defining placements of temporary scratch buffers in the at least one free area of the heap memory zone according to the execution order of the layers.

Method for memory allocation during execution of a neural network

According to one aspect, the disclosure proposes a method for detecting events or features in physical signals by implementing an artificial neural network. The method includes evaluating the probability of presence of the event or feature by implementing the artificial neural network. The method includes implementing the artificial neural network in a nominal mode and to which a physical signal having a first so-called nominal resolution is fed, as long as the probability of the presence of the event or feature is below a threshold. The method further includes implementing the artificial neural network in a reduced consumption mode with a reduced resolution, as long as the probability of the presence of the event or feature is above the threshold. The reduced resolution is lower than the first resolution.

Process for monitoring at least one element in a temporal succession of physical signals

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.

System and method for modifying integer and fractional portion sizes of a parameter of a neural network

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.

Filtering data in orthogonal directions through a convolutional neural network

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.

Method and computational tool for determining transfer functions between pairs of successive layers of a neural network

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.

Method and device for determining a global memory size of a global memory size for a neural network

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.

Method and device for determining memory size

A method is provided for monitoring scene detection by an apparatus detecting scenes from among a set of possible reference scenes. It includes an assignment of an identifier to each reference scene, detection of scenes from among the set of possible reference scenes at successive instants of detection with the aid of at least one classification algorithm, and a sliding time filtering processing of these detected current scenes over a filtering window of size M, based on the identifier of each new detected current scene taken into account in the window and a confidence probability associated with this new detected current scene, the output of the filtering processing successively delivering filtered detected scenes.

Method and apparatus for monitoring scene detection

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.

Method for managing a real time detection of a scene by a wireless communication apparatus and corresponding apparatus

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

A method can be used for managing a real-time detection related to a scene. A succession of steps of scene detection is spaced apart by time intervals. A time interval separating a current step of scene detection from a previous step of scene detection is adjusted according to an adjustment criterion linked to a previous scene actually detected. The succession of steps and the adjustment are performed by a wireless communication apparatus.

Method for managing a real time detection of a scene by a wireless communication apparatus

A method is for processing a channel analog signal coming from a transmission channel. The method may include converting the channel analog signal into a channel digital signal, and detecting a state of the transmission channel based on the channel digital signal to detect whether the transmission channel is, over an interval of time, one or more of linear and time invariant and linear and cyclostationary.

Receiver and method for processing a signal coming from a transmission channel

A control method for real-time scene detection by a wireless communication apparatus equipped with at least one environmental measurement sensor is disclosed. A temporal adjustment of the instants of activation of the detection is based on measurement values delivered by the at least one environmental measurement sensor at instants of measurement.

Control method for real-time scene detection by a wireless communication apparatus

A method is for processing an analog signal coming from a transmission channel. The analog signal may include a useful signal modulated on a sub-set of carriers. The method may include analog-to-digital converting of the analog signal into a digital signal, and synchronization processing the digital signal. The synchronizing may include determining, in a time domain, a limited number of coefficients of a predictive filter from an autoregressive model of the digital signal, and filtering the digital signal in the time domain by a digital finite impulse response filter with coefficients based upon the limited number of coefficients to provide a filtered digital signal. The method may include detecting of an indication allowing a location in the frame structure to be identified, using the filtered digital signal and a reference signal.

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Nice, France

Pierre Demaj