WO2022179075A1 - Procédé et appareil de traitement de données, dispositif informatique et support de stockage - Google Patents
Procédé et appareil de traitement de données, dispositif informatique et support de stockage Download PDFInfo
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- WO2022179075A1 WO2022179075A1 PCT/CN2021/115789 CN2021115789W WO2022179075A1 WO 2022179075 A1 WO2022179075 A1 WO 2022179075A1 CN 2021115789 W CN2021115789 W CN 2021115789W WO 2022179075 A1 WO2022179075 A1 WO 2022179075A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
Definitions
- the present disclosure relates to the field of computer technology, and in particular, to a data processing method, apparatus, computer device, and storage medium.
- the method before the determining of the target feature elements and target weight elements corresponding to the multiple processing cycles respectively, the method further includes: based on the size of the PE array, performing the processing on the original to-be-processed image feature matrix and the original weight matrix. The size is transformed to obtain the feature matrix of the to-be-processed image and the weight matrix.
- the use of the PE array to perform a preset operation on the first operand and the second operand stored in the PE array to obtain intermediate processing data corresponding to the processing cycle Including: in the processing cycle, weighted summation is performed on each row of target feature elements in the first operand and each row of weight elements in the second operand to obtain the different weight matrices Corresponding intermediate data; based on the intermediate data corresponding to the different weight matrices, obtain the intermediate processing data corresponding to the processing period.
- the original to-be-processed image feature matrix may be size-transformed, or not.
- the size of the feature map is smaller than the size of the PE array, then only a part of the PE in the PE array will be used during the process of using the PE array, while Not all will be used.
- the fourth column a1, a2, a3, and a4 are weighted and summed to obtain the intermediate data O4 1 corresponding to the weight matrix W4.
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- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Image Processing (AREA)
Abstract
Procédé et appareil de traitement de données, dispositif informatique et support de stockage. Le procédé consiste : à déterminer, à partir d'une matrice de caractéristiques d'image à traiter et de matrices de pondération, des éléments de caractéristiques cibles et des éléments de pondération cibles correspondant à une pluralité de cycles de traitement, ladite matrice de caractéristiques d'image correspondant à une pluralité de matrices de pondération ; en réponse à l'arrivée d'un quelconque cycle de traitement, à acquérir, par chaque moteur de traitement (PE) dans un réseau PE, un élément de caractéristiques cible correspondant et un élément de pondération cible correspondant du cycle de traitement, et à effectuer une opération prédéfinie, de manière à obtenir des données de traitement intermédiaires, pour tout cycle de traitement, des éléments de caractéristiques cibles dans le réseau PE comprenant des éléments de caractéristiques répétés, et des éléments de pondération cible correspondant aux éléments de caractéristiques répétés étant des éléments de pondération, correspondant aux éléments de caractéristiques répétés, dans différentes matrices de pondération ; et sur la base de données de traitement intermédiaire correspondant à la pluralité de cycles de traitement, à obtenir des données de résultat du traitement de ladite matrice de caractéristiques d'image.
Applications Claiming Priority (2)
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CN202110221235.3 | 2021-02-26 | ||
CN202110221235.3A CN112966729B (zh) | 2021-02-26 | 2021-02-26 | 一种数据处理方法、装置、计算机设备及存储介质 |
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WO2022179075A1 true WO2022179075A1 (fr) | 2022-09-01 |
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PCT/CN2021/115789 WO2022179075A1 (fr) | 2021-02-26 | 2021-08-31 | Procédé et appareil de traitement de données, dispositif informatique et support de stockage |
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CN (1) | CN112966729B (fr) |
WO (1) | WO2022179075A1 (fr) |
Families Citing this family (2)
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CN112966729B (zh) * | 2021-02-26 | 2023-01-31 | 成都商汤科技有限公司 | 一种数据处理方法、装置、计算机设备及存储介质 |
CN113253336B (zh) * | 2021-07-02 | 2021-10-01 | 深圳市翩翩科技有限公司 | 一种基于深度学习的地震预测方法和*** |
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CN108805275B (zh) * | 2017-06-16 | 2021-01-22 | 上海兆芯集成电路有限公司 | 可编程设备及其操作方法和计算机可用介质 |
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- 2021-08-31 WO PCT/CN2021/115789 patent/WO2022179075A1/fr unknown
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