公开(公告)号：US11593664B2

公开(公告)日：2023-02-28

申请号：US16917414

申请日：2020-06-30

Applicant: STMicroelectronics S.r.l. ,  STMicroelectronics (Rousset) SAS

Inventor： Laurent Folliot ,  Pierre Demaj ,  Emanuele Plebani

IPC: G06N3/10 ,  G06N3/04 ,  G06N3/08

Abstract: 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.

公开(公告)号：US11609851B2

公开(公告)日：2023-03-21

申请号：US17229161

申请日：2021-04-13

Applicant: STMicroelectronics (Rousset) SAS ,  STMicroelectronics S.r.l.

Inventor： Laurent Folliot ,  Emanuele Plebani ,  Mirko Falchetto

IPC: G06F12/06 ,  G06N3/04

Abstract: 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.

公开(公告)号：US20210342265A1

公开(公告)日：2021-11-04

申请号：US17229161

申请日：2021-04-13

Applicant: STMicroelectronics (Rousset) SAS ,  STMicroelectronics S.r.l.

Inventor： Laurent Folliot ,  Emanuele Plebani ,  Mirko Falchetto

IPC: G06F12/06 ,  G06N3/04

Abstract: 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.

公开(公告)号：US20210012208A1

公开(公告)日：2021-01-14

申请号：US16917414

申请日：2020-06-30

Applicant: STMicroelectronics S.r.l. ,  STMicroelectronics (Rousset) SAS

Inventor： Laurent Folliot ,  Pierre Demaj ,  Emanuele Plebani

IPC: G06N3/10 ,  G06N3/04 ,  G06N3/08

Abstract: 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.

公开(公告)号：US11537840B2

公开(公告)日：2022-12-27

申请号：US16189264

申请日：2018-11-13

Applicant: STMICROELECTRONICS S.r.l.

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

IPC: G06N3/04 ,  G06N3/08 ,  G06N5/00 ,  G06N3/063

Abstract: 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.

公开(公告)号：US20160307068A1

公开(公告)日：2016-10-20

申请号：US14958041

申请日：2015-12-03

Applicant: STMicroelectronics S.r.l.

Inventor： Danilo Pietro Pau ,  Emanuele Plebani ,  Luca Paliotto

IPC: G06K9/62

CPC classification number: G06K9/6223 ,  G06F16/583 ,  G06K9/6215

Abstract: 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.

Abstract translation: 数字图像处理电路将一组图像聚集成一组第一组图像和一组非聚簇图像。 第一集群的集合被合并，产生一组第二组图像。 非集群图像集中的图像被分配给该组第二聚类图像和离群图像簇中的一个簇。 基于对图像中的对象的检测，聚类图像可以被划分成子簇。

公开(公告)号：US11960988B2

公开(公告)日：2024-04-16

申请号：US15903605

申请日：2018-02-23

Applicant: STMICROELECTRONICS S.r.l.

Inventor： Emanuele Plebani ,  Danilo Pietro Pau

IPC: G06N3/08 ,  G06N3/084 ,  G06N7/01

CPC classification number: G06N3/08 ,  G06N3/084 ,  G06N7/01

Abstract: 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.

公开(公告)号：US11461142B2

公开(公告)日：2022-10-04

申请号：US16924091

申请日：2020-07-08

Applicant: STMicroelectronics S.r.l.

Inventor： Emanuele Plebani ,  Mirko Falchetto ,  Danilo Pietro Pau

IPC: G06F9/50 ,  G06F9/48 ,  G06F9/32 ,  G06N3/02 ,  G06N3/04 ,  G06N3/063 ,  G06F12/06 ,  G06F9/54

Abstract: 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.

公开(公告)号：US10330779B2

公开(公告)日：2019-06-25

申请号：US15692689

申请日：2017-08-31

Applicant: STMICROELECTRONICS S.r.l.

Inventor： Danilo Pietro Pau ,  Emanuele Plebani

IPC: G01S7/04 ,  G01S7/484 ,  G01S17/89 ,  G01S17/93 ,  G01S7/48 ,  G01S7/497 ,  G01S7/481 ,  G01S7/486 ,  G06K9/00 ,  H05B37/02

Abstract: 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.