patents

2024

1. US

   Learning method, corresponding system, device and computer program product to update classifier model parameters of a classification device

   Emanuele Plebani, and Danilo Pietro Pau

   Apr 2024

   US Patent 11,960,988

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   A classification device receives sensor data from a set of sensors and generates, using a context classifier having a set of classifier model parameters, a set of raw predictions based on the received sensor data. Temporal filtering and heuristic filtering are applied to the raw predictions, producing filtered predictions. A prediction error is generated from the filtered predictions, and model parameters of the set of classifier model parameters are updated based on said prediction error. The classification device may be a wearable device.

2023

1. US

   Device and method for allocating intermediate data from an artificial neural network

   Laurent Folliot, Emanuele Plebani, and Mirko Falchetto

   Mar 2023

   US Patent 11,609,851

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   According to one aspect, a method for determining, for a memory allocation, placements in a memory area of data blocks generated by a neural network, comprises a development of an initial sequence of placements of blocks, each placement being selected from several possible placements, the initial sequence being defined as a candidate sequence, a development of at least one modified sequence of placements from a replacement of a given placement of the initial sequence by a memorized unselected placement, and, if the planned size of the memory area obtained by this modified sequence is less than that of the memory area of the candidate sequence, then this modified sequence becomes the candidate sequence, the placements of the blocks for the allocation being those of the placement sequence defined as a candidate sequence once each modified sequence has been developed.

2. US

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

   Laurent Folliot, Pierre Demaj, and Emanuele Plebani

   Feb 2023

   US Patent 11,593,664

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   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.

2022

1. US

   Method, system, and computer program product to employ a multi-layered neural network for classification

   Danilo Pietro Pau, Emanuele Plebani, Fabio Giuseppe De Ambroggi, Floriana Guido, and Angelo Bosco

   Dec 2022

   US Patent 11,537,840

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   A neural network classifies an input signal. For example, an accelerometer signal may be classified to detect human activity. In a first convolutional layer, two-valued weights are applied to the input signal. In a first two-valued function layer coupled at input to an output of the first convolutional layer, a two-valued function is applied. In a second convolutional layer coupled at input to an output of the first two-valued functional layer, weights of the second convolutional layer are applied. In a fully-connected layer coupled at input to an output of the second convolutional layer, two-valued weights of the fully connected layer are applied. In a second two-valued function layer coupled at input to an output of the fully connected layer, a two-valued function of the second two-valued function layer is applied. A classifier classifies the input signal based on an output signal of second two-valued function layer.

2. US

   Automatic memory management method, corresponding micro-controller unit and computer program product

   Emanuele Plebani, Mirko Falchetto, and Danilo Pietro Pau

   Oct 2022

   US Patent 11,461,142

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   Methods, microprocessors, and systems are provided for implementing an artificial neural network. Data buffers in virtual memory are coupled to respective processing layers in the artificial neural network. An ordered visiting sequence of layers of the artificial neural network is obtained. A virtual memory allocation schedule is produced as a function of the ordered visiting sequence of layers of the artificial neural network, the schedule including a set of instructions for memory allocation and deallocation operations applicable to the data buffers. A physical memory configuration dataset is computed as a function of the virtual memory allocation schedule for the artificial neural network, the dataset including sizes and addresses of physical memory locations for the artificial neural network.

3. US App

   Computer-implemented data processing method, micro-controller system and computer program product

   Emanuele Plebani

   Jan 2022

   US Patent App. 17/369,417

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   A computer-implemented method applies a pooling operator to an input array of data, the pooling operator having an absorbing element value and a set of pooling parameters. A size of an output buffer is computer as a function of the set of pooling parameters. The elements of the output buffer are initialized to the value of the absorbing element of the pooling operator. The output array of data is generated by, for a plurality of iterations associated with respective pooling windows: associating, as a function of the pooling parameters, elements of the input array of a pooling window with output elements of the output buffer; and combining, for each output element of the output buffer, the respective input elements associated with the output element. The combining may include determining a combination of respective elements of the output buffer with the input elements associated with the output elements.

2019

1. US

   Method of clustering digital images, corresponding system, apparatus and computer program product

   Danilo Pietro Pau, Emanuele Plebani, and Luca Paliotto

   Nov 2019

   US Patent 10,489,681

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   Digital image processing circuitry clusters a set of images into a set of first clusters of images and a set of unclustered images. The set of first clusters are merged, generating a set of second clusters of images. Images in the set of unclustered images are assigned to one of a cluster of the set of second clusters of images and an outlier image cluster. The clustered images may be partitioned into subclusters based on detection of objects in the images.

2. US

   Laser beam control method, corresponding device, apparatus and computer program product

   Danilo Pietro Pau, and Emanuele Plebani

   Jun 2019

   US Patent 10,330,779

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   A laserbeam light source is controlled to avoid light sensitive regions around the laserbeam light source. One or more laserlight-sensitive regions are identified based on images of an area around the laserbeam light source, and indications of positions corresponding to the laserlight-sensitive regions are generated. The laserbeam light source is controlled based on the indications of the positions. The laserbeam light source may be controlled to deflect a laserlight beam away from laserlight-sensitive regions, to reduce an intensity of a laserlight beam directed towards a laserlight-sensitive region, etc. Motion estimation may be used to generate the indications of positions corresponding to the laserlight-sensitive regions.

2018

1. US App

   Artificial neural networks for human activity recognition

   Danilo Pietro Pau, and Emanuele Plebani

   Mar 2018

   US Patent App. 15/280,463

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   Human activities are classified based on activity-related data and an activity-classification model trained using a classification-equalized training data set. A classification signal is generated based on the classifications. The classification-equalized training data set, may, for example, includes a first class having a first sequence length and a number of samples \(N\), and one or more additional classes each having a respective sequence length \(t_j\) and a respective number of samples \(N_j\) determined based on the number of samples \(N\) of the first class. For example, a respective sequence length \(t_j\) and a respective number of samples \(N_j\) which satisfy: (i) \(N_j>N\), for sequence length \(t_j\); and (ii) \(N_j<N\), for \(t_j−1\). The activity-related data may include one or more of acceleration data, orientation data, position data, and physiological data.