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Artificial neural network processing method and system

A technology for processing layers and grids, which is applied in the field of artificial neural network processing and systems, and can solve problems such as high computing costs

Pending Publication Date: 2022-01-25
STMICROELECTRONICS SRL
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] Processing layers can involve extensive dot product operations between weights and feature vectors, potentially resulting in high computational costs

Method used

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  • Artificial neural network processing method and system
  • Artificial neural network processing method and system
  • Artificial neural network processing method and system

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Embodiment Construction

[0051] In the ensuing description, one or more specific details are illustrated in order to provide an in-depth understanding of the embodiment examples of the present specification. Embodiments may be obtained without one or more of the specific details or with other methods, components, materials, etc. In other instances, known structures, materials, or operations are not shown or described in detail so as not to obscure aspects of the embodiments.

[0052] References to "an embodiment" or "one embodiment" in the framework of this specification are intended to indicate that a particular configuration, structure or characteristic about the embodiments is included in at least one embodiment. Thus, phrases such as "in an embodiment" or "in one embodiment," which may be present at one or more points in this specification, are not necessarily referring to the same embodiment.

[0053] Furthermore, particular conformations, structures or properties may be combined in any suitable...

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Abstract

Various embodiments of the present disclosure relate to a method comprising: providing an ANN processing stage having a plurality of processing layers, the processing layers having respective parameters including at least one weight parameter set, at least one input / output activation parameter, and at least one activation function parameter; setting a dimension parameter of a grid to an integer value, the grid having a plurality of grid points and identified by a set of base vectors; selecting a weight parameter set of a corresponding processing layer; vectorizing the selected set of weight parameters, thereby producing a set of weight vectors, the set of weight vectors being arranged as terms of a matrix of weight vectors; normalizing the weight vector matrix; performing grid vector quantization (LVQ) processing on the normalized weight vector matrix to generate a codebook of code words; and indexing codewords of a codebook as a function of a grid by encoding the codewords into the grid to generate respective index tuples. Normalizing includes solving an optimization problem having a first term and a regularization term [Omega] L.

Description

technical field [0001] For example, the present specification relates to methods and systems for artificial neural network (ANN) processing, such as, for example, convolutional neural network (CNN) processing. [0002] For example, one or more embodiments may be applied to a hardware accelerator engine (eg, such as a Neural Processing Unit (NPU)) configured to perform ANN processing. Background technique [0003] Convolutional (artificial) neural networks, or CNNs for short, include computer-based tools that utilize deep learning algorithms to perform image processing tasks. [0004] Classifying images of big cats as leopards or jaguars can be mentioned as an example of such processing. [0005] A CNN comprises a plurality of layers, such as a plurality of (hidden) processing layers coupled to an input layer and configured to apply data processing to image tensors received therefrom. The processing layers may include, for example, convolution and / or pooling, and the CNN ma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/082G06N3/045
Inventor D·卡雷拉M·科莱拉G·德索利G·博拉基B·罗西P·弗拉尼托L·弗里托利
Owner STMICROELECTRONICS SRL
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