WO2021147196A1 - Procédé, appareil et dispositif d'opération de convolution, et support de stockage - Google Patents

Procédé, appareil et dispositif d'opération de convolution, et support de stockage Download PDF

Info

Publication number
WO2021147196A1
WO2021147196A1 PCT/CN2020/087105 CN2020087105W WO2021147196A1 WO 2021147196 A1 WO2021147196 A1 WO 2021147196A1 CN 2020087105 W CN2020087105 W CN 2020087105W WO 2021147196 A1 WO2021147196 A1 WO 2021147196A1
Authority
WO
WIPO (PCT)
Prior art keywords
matrix
convolution
sample data
intermediate matrix
generate
Prior art date
Application number
PCT/CN2020/087105
Other languages
English (en)
Chinese (zh)
Inventor
董刚
赵雅倩
李仁刚
杨宏斌
刘海威
Original Assignee
苏州浪潮智能科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 苏州浪潮智能科技有限公司 filed Critical 苏州浪潮智能科技有限公司
Publication of WO2021147196A1 publication Critical patent/WO2021147196A1/fr

Links

Images

Classifications

    • 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
    • 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
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G06F17/153—Multidimensional correlation or convolution
    • 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
    • G06N3/08—Learning methods

Definitions

  • the present invention relates to the field of deep learning, in particular to a convolution operation method, device, equipment and storage medium.
  • Deep learning refers to the internal laws and representation levels of learning sample data. Its ultimate goal is to enable the machine to have the ability to analyze and learn like humans, recognize text, images, and sound data, and perform convolution operations on the sample data. Feature extraction is currently an important means to realize deep learning.
  • the operation of inner product of sample data and convolution kernel in different data windows in an image is called convolution, and its calculation process is also called filtering.
  • the essence is to extract the characteristics of different frequency bands of the image.
  • the convolution kernel is also called a filter. It is a set of neurons with fixed weights, usually a square two-dimensional matrix. The matrix stores the coefficients for processing the data in the receptive field.
  • the filtering of a convolution kernel can be used Extract specific features, for example, you can extract the contours of objects in the image, the color depth, and so on. Because the matrix elements of the sample data currently acquired in the data window are often more than the matrix elements of the convolution kernel, and the number of matrix elements varies greatly, it is difficult to ensure the overall efficiency of the convolution kernel for convolution operations on the sample data.
  • the purpose of the present invention is to provide a convolution operation method, device, equipment and storage medium to relatively ensure the overall efficiency of the convolution operation process.
  • the present invention provides a convolution operation method, including:
  • performing a convolution operation on the first intermediate matrix through the second intermediate matrix includes:
  • reading the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory includes:
  • performing a convolution operation on the first intermediate matrix through the second intermediate matrix includes:
  • a convolution operation is performed on the first intermediate matrix through the second intermediate matrix.
  • the method further includes:
  • the convolution result is stored in the storage location corresponding to the sample data matrix in the memory.
  • performing an expansion operation on the sample data matrix to generate the first intermediate matrix includes:
  • the corresponding first transposed data columns are combined into a first intermediate matrix.
  • performing an expansion operation on the convolution kernel matrix to generate the second intermediate matrix includes:
  • a second intermediate matrix is combined based on a plurality of second transposed data columns.
  • performing an expansion operation on the sample data matrix to generate the first intermediate matrix includes:
  • an expansion operation is sequentially performed to generate a first intermediate matrix.
  • the present invention also provides a convolution operation device, including:
  • the matrix reading module is used to read the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory;
  • the preprocessing module is used to perform an expansion operation on the sample data matrix to generate a first intermediate matrix, and perform an expansion operation on the convolution kernel matrix to generate a second intermediate matrix, the number of rows and columns between the first intermediate matrix and the second intermediate matrix The numbers are the same;
  • the convolution execution module is configured to perform a convolution operation on the first intermediate matrix through the second intermediate matrix and generate a convolution result.
  • the convolution execution module includes:
  • the matrix product module is used to perform matrix multiplication operations on each first intermediate matrix through the second intermediate matrix and generate a corresponding result matrix
  • the accumulation module is used to perform accumulation operations on each result matrix.
  • the matrix reading module includes:
  • the memory reading module is used to read the sample data matrix in the DDR memory, and read the convolution kernel matrix corresponding to the sample data matrix in the HBM2 memory.
  • the present invention also provides a convolution operation device, including:
  • Memory used to store computer programs
  • the processor is used to implement the steps of the above-mentioned convolution operation method when the computer program is executed.
  • the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the convolution operation method as described above are realized.
  • the convolution operation method provided by the present invention first reads the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • the number of rows or columns of the sample data matrix is equal to the number of rows of the convolution kernel matrix. Therefore, the expansion operation is performed on the sample data matrix and the convolution kernel matrix respectively to generate the first intermediate matrix and the second intermediate matrix, and the number of rows and columns of the first intermediate matrix and the second intermediate matrix are the same, and finally through the convolution
  • the second intermediate matrix obtained by the expansion of the kernel matrix performs a convolution operation on the first intermediate matrix obtained by the expansion of the sample data matrix to generate a corresponding convolution result.
  • the first intermediate matrix generated is equivalent to the sample data matrix
  • the second intermediate matrix generated is equivalent to the convolution kernel matrix. Therefore, the second intermediate matrix is equivalent to the convolution kernel matrix.
  • Performing a convolution operation on the first intermediate matrix by the matrix is equivalent to performing a convolution operation on the sample data matrix by the convolution kernel matrix, and can increase the amount of convolution data between the two matrices per unit time, thereby relatively ensuring the convolution operation process Overall efficiency.
  • the present invention also provides a convolution operation device, equipment and storage medium, and the beneficial effects are the same as those described above.
  • FIG. 1 is a flowchart of a convolution operation method disclosed in an embodiment of the present invention
  • Figure 2.a is a schematic diagram of the expansion operation of a sample data matrix in a specific application scenario disclosed in an embodiment of the present invention
  • Figure 2.b is a schematic diagram of the expansion operation of a convolution kernel matrix in a specific application scenario disclosed in an embodiment of the present invention
  • FIG. 3 is a flowchart of a specific convolution operation method disclosed in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of the composition structure of a convolution operation device disclosed in an embodiment of the present invention.
  • the operation of inner product of sample data and convolution kernel in different data windows in an image is called convolution, and its calculation process is also called filtering.
  • the essence is to extract the characteristics of different frequency bands of the image.
  • the convolution kernel is also called a filter. It is a set of neurons with fixed weights, usually a square two-dimensional matrix. The matrix stores the coefficients for processing the data in the receptive field.
  • the filtering of a convolution kernel can be used Extract specific features, for example, you can extract the contours of objects in the image, the color depth, and so on. Because the matrix elements of the sample data currently acquired in the data window are often more than the matrix elements of the convolution kernel, and the number of matrix elements varies greatly, it is difficult to ensure the overall efficiency of the convolution kernel for convolution operations on the sample data.
  • the core of the present invention is to provide a convolution operation method to relatively ensure the overall efficiency of the convolution operation process.
  • an embodiment of the present invention discloses a convolution operation method, including:
  • Step S10 Read the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • the sample data matrix read in this step can be a data matrix extracted from samples including but not limited to pictures, audio, text, etc.
  • the convolution kernel matrix corresponding to the sample data matrix is the pair of samples.
  • the data matrix is a matrix for feature extraction.
  • the elements in the convolution kernel matrix are set according to the specific types of features extracted in the sample data matrix, and the convolution kernel matrix generates feature images by performing convolution operations on the sample data matrix, that is, volume
  • the feature image can reflect the distribution state of the corresponding type of feature in the sample data matrix.
  • the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix are read in the memory. Specifically, the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix can be obtained in the same memory, or It is to obtain the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in two independent memories.
  • Step S11 Perform an expansion operation on the sample data matrix to generate a first intermediate matrix, and perform an expansion operation on the convolution kernel matrix to generate a second intermediate matrix.
  • the number of rows and columns between the first intermediate matrix and the second intermediate matrix are the same .
  • the focus of this embodiment is to obtain the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix, and before performing the convolution operation on the sample data matrix with the convolution kernel matrix, first perform the convolution operation on the sample data matrix and the convolution kernel.
  • the matrix is preprocessed, that is, the sample data matrix and the convolution kernel matrix are expanded respectively.
  • the purpose of the expansion operation is to obtain the first intermediate matrix and the second intermediate matrix with the same number of rows and columns. Among them, the first intermediate matrix is equivalent to the sample data matrix, and the second intermediate matrix is equivalent to the convolution kernel matrix.
  • the expansion operation in this step may specifically be expanded by row.
  • Step S12 Perform a convolution operation on the first intermediate matrix through the second intermediate matrix, and generate a convolution result.
  • a convolution operation is further performed on the first intermediate matrix through the second intermediate matrix to generate a corresponding convolution result.
  • the convolution operation method provided by the present invention first reads the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • the number of rows or columns of the sample data matrix is equal to the number of rows of the convolution kernel matrix. Therefore, the expansion operation is performed on the sample data matrix and the convolution kernel matrix respectively to generate the first intermediate matrix and the second intermediate matrix, and the number of rows and columns of the first intermediate matrix and the second intermediate matrix are the same, and finally through the convolution
  • the second intermediate matrix obtained by the expansion of the kernel matrix performs a convolution operation on the first intermediate matrix obtained by the expansion of the sample data matrix to generate a corresponding convolution result.
  • the first intermediate matrix generated is equivalent to the sample data matrix
  • the second intermediate matrix generated is equivalent to the convolution kernel matrix. Therefore, the second intermediate matrix is equivalent to the convolution kernel matrix.
  • Performing a convolution operation on the first intermediate matrix by the matrix is equivalent to performing a convolution operation on the sample data matrix by the convolution kernel matrix, and can increase the amount of convolution data between the two matrices per unit time, thereby relatively ensuring the convolution operation process Overall efficiency.
  • performing an expansion operation on the sample data matrix to generate a first intermediate matrix includes:
  • the corresponding first transposed data columns are combined into a first intermediate matrix.
  • the matrix transposition and splicing method since the matrix transposition and splicing method is adopted in this embodiment, the first intermediate matrix obtained by the transformation can be characterized by row-first calculation, and the amount of intermediate result data generated in the calculation process is small. Advantages, so it can achieve the effect of reducing hardware resource overhead.
  • performing an expansion operation on the convolution kernel matrix to generate a second intermediate matrix includes:
  • a second intermediate matrix is combined based on a plurality of second transposed data columns.
  • this embodiment can generate the second intermediate matrix relatively efficiently based on the row and column size of the first intermediate matrix, which improves the overall efficiency of the convolution operation.
  • the sample data matrix is a 3x11 matrix
  • the first intermediate matrix after the expansion operation is a 9x9 arrangement.
  • the upper three rows of the 9x9 arrangement are obtained by dividing the first row of the 3x11 format three times, each time taking 9 data.
  • the initial positions of the three selected data are the first, second, and third data respectively.
  • the lower six rows of the 9x9 arrangement can be obtained.
  • the convolution kernel matrix is a 3x3 matrix
  • the expansion operation is to arrange the 3x3 data into a column in the order of rows, and further expand it into 9 columns.
  • the present invention also provides the following series of preferred embodiments.
  • An embodiment of the present invention discloses a convolution operation method, including:
  • Step S20 Read the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • Step S21 Perform an expansion operation on the sample data matrix to generate a first intermediate matrix, and perform an expansion operation on the convolution kernel matrix to generate a second intermediate matrix.
  • the number of rows and columns between the first intermediate matrix and the second intermediate matrix are the same .
  • Step S22 Perform a matrix multiplication operation on each first intermediate matrix respectively through the second intermediate matrix and generate a corresponding result matrix.
  • Step S23 Perform an accumulation operation on each result matrix and generate a convolution result.
  • the number of sample data matrices is greater than 1, the number of first intermediate matrices generated by performing the expansion operation based on the sample data matrix is also greater than 1, so the second intermediate matrix corresponding to the convolution kernel matrix needs to be the same as all
  • the first intermediate matrix performs matrix multiplication operations and generates corresponding result matrices, and then accumulates each result matrix to generate a convolution result of all sample data matrices.
  • This implementation can relatively ensure the number of sample data matrices When it is greater than 1, the overall accuracy of the convolution operation performed on the sample data matrix.
  • reading the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory includes:
  • the sample data matrix and the convolution kernel matrix are obtained from two different memories, that is, the sample data matrix is read in the DDR memory, and the sample data matrix corresponding to the sample data matrix is read in the HBM2 memory.
  • Convolution kernel matrix where DDR memory and HBM2 memory can belong to the same arithmetic chip.
  • DDR memory and HBM2 memory belong to the FPGA chip.
  • the FPGA chip obtains the sample data matrix from the local DDR chip.
  • the local HBM2 memory obtains the convolution kernel matrix, and performs the convolution operation of the convolution kernel matrix on the sample data matrix in the FPGA chip.
  • Both the DDR memory and the HBM2 memory in this embodiment can achieve a higher data transmission rate at the same bus frequency as the SDRAM memory, so this embodiment can further improve the overall efficiency of the convolution operation.
  • performing a convolution operation on the first intermediate matrix through the second intermediate matrix includes:
  • a convolution operation is performed on the first intermediate matrix through the second intermediate matrix.
  • the DSP arithmetic array also called a digital signal processor, is a microprocessor with a special structure.
  • the internal structure of the DSP chip is separated from the program and the data. It has a hardware multiplier and widely adopts pipeline operation.
  • the provided DSP instructions can be used to quickly implement various digital signal processing algorithms. Therefore, this implementation mode passes through the DSP arithmetic array.
  • the second intermediate matrix performs the convolution operation on the first intermediate matrix, which can relatively improve the overall efficiency of the second intermediate matrix performing the convolution operation on the first intermediate matrix.
  • the method further includes:
  • the convolution result is stored in the storage location corresponding to the sample data matrix in the memory.
  • the convolution result is further stored in the storage location corresponding to the sample data matrix in the memory.
  • the purpose is to cover the original sample data in the memory by the convolution result.
  • the matrix is used to ensure the space availability of the memory, thereby avoiding the waste of memory space, thereby reducing the storage pressure of the memory, and ensuring the overall stability of the convolution operation.
  • performing an expansion operation on the sample data matrix to generate the first intermediate matrix includes:
  • an expansion operation is sequentially performed to generate a first intermediate matrix.
  • this embodiment performs the expansion operation in sequence based on each element in the target dimension in the sample data to generate the first intermediate matrix, and then the first intermediate matrix is processed through the second intermediate matrix.
  • the intermediate matrix performs a convolution operation, which can sequentially perform the convolution operation between the second intermediate matrix and the first intermediate matrix with each element in the target dimension as a unit, relatively reducing the second intermediate matrix and the second intermediate matrix corresponding to the same element in the target dimension. The amount of intermediate data generated when the first intermediate matrix performs the convolution operation, thereby achieving the effect of reducing hardware resource overhead.
  • the present invention also provides a convolution operation device.
  • FIG. 4 shows a schematic diagram of the composition structure of an embodiment of a convolution operation device, and the device includes:
  • the matrix reading module 10 is used for reading the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • the preprocessing module 11 is used to perform an expansion operation on the sample data matrix to generate a first intermediate matrix, and perform an expansion operation on the convolution kernel matrix to generate a second intermediate matrix, the number of rows between the first intermediate matrix and the second intermediate matrix and The number of columns is the same.
  • the convolution execution module 12 is configured to perform a convolution operation on the first intermediate matrix through the second intermediate matrix, and generate a convolution result.
  • the convolution operation device provided by the present invention first reads the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • the number of rows or columns of the sample data matrix is equal to the number of rows of the convolution kernel matrix. Therefore, the expansion operation is performed on the sample data matrix and the convolution kernel matrix respectively to generate the first intermediate matrix and the second intermediate matrix, and the number of rows and columns of the first intermediate matrix and the second intermediate matrix are the same, and finally through the convolution
  • the second intermediate matrix obtained by the expansion of the kernel matrix performs a convolution operation on the first intermediate matrix obtained by the expansion of the sample data matrix to generate a corresponding convolution result.
  • the second intermediate matrix generated by the device after the expansion operation of the sample data matrix and the convolution kernel matrix is equivalent to the sample data matrix
  • the second intermediate matrix generated is equivalent to the convolution kernel matrix
  • the second intermediate matrix is equivalent to the convolution kernel matrix.
  • the convolution execution module includes:
  • the matrix product module is used to perform matrix multiplication operations on each first intermediate matrix through the second intermediate matrix and generate a corresponding result matrix
  • the accumulation module is used to perform accumulation operations on each result matrix.
  • the matrix reading module includes:
  • the memory reading module is used to read the sample data matrix in the DDR memory, and read the convolution kernel matrix corresponding to the sample data matrix in the HBM2 memory.
  • the present invention also provides a convolution operation device, including:
  • Memory used to store computer programs
  • the processor is used to implement the steps of the above-mentioned convolution operation method when the computer program is executed.
  • the convolution operation device provided by the present invention first reads the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory.
  • the number of rows or columns of the sample data matrix is equal to the number of rows of the convolution kernel matrix. Therefore, the expansion operation is performed on the sample data matrix and the convolution kernel matrix respectively to generate the first intermediate matrix and the second intermediate matrix, and the number of rows and columns of the first intermediate matrix and the second intermediate matrix are the same, and finally through the convolution
  • the second intermediate matrix obtained by the expansion of the kernel matrix performs a convolution operation on the first intermediate matrix obtained by the expansion of the sample data matrix to generate a corresponding convolution result.
  • the second intermediate matrix generated by this device after the expansion operation of the sample data matrix and the convolution kernel matrix is equivalent to the sample data matrix
  • the second intermediate matrix generated is equivalent to the convolution kernel matrix
  • the second intermediate matrix is equivalent to the convolution kernel matrix.
  • the present invention also provides a computer-readable storage medium with a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the convolution operation method as described above are realized.
  • the computer-readable storage medium provided by the present invention first reads the sample data matrix and the convolution kernel matrix corresponding to the sample data matrix in the memory, the number of rows or columns of the sample data matrix and the number of rows of the convolution kernel matrix Consistent, and then perform the expansion operation on the sample data matrix and the convolution kernel matrix to generate the first intermediate matrix and the second intermediate matrix, and the number of rows and columns of the first intermediate matrix and the second intermediate matrix are the same, and finally pass the convolution
  • the second intermediate matrix obtained by the expansion of the product kernel matrix performs a convolution operation on the first intermediate matrix obtained by the expansion of the sample data matrix to generate a corresponding convolution result.
  • the first intermediate matrix generated is equivalent to the sample data matrix
  • the second intermediate matrix generated is equivalent to the convolution kernel matrix.
  • Performing a convolution operation on the first intermediate matrix through the second intermediate matrix is equivalent to performing a convolution operation on the sample data matrix by the convolution kernel matrix, and can increase the amount of convolution data between the two matrices per unit time, thereby relatively ensuring The overall efficiency of the convolution operation process.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Algebra (AREA)
  • Databases & Information Systems (AREA)
  • Complex Calculations (AREA)

Abstract

Un procédé, un appareil et un dispositif d'opération de convolution, et un support de stockage. Le procédé consiste : à lire, à partir d'une mémoire, une matrice de données d'échantillon et une matrice de noyau de convolution correspondant à la matrice de données d'échantillon ; à exécuter une opération d'expansion sur la matrice de données d'échantillon pour générer une première matrice intermédiaire, et à exécuter une opération d'expansion sur la matrice de noyau de convolution pour générer une seconde matrice intermédiaire, le nombre de rangées et le nombre de colonnes entre la première matrice intermédiaire et la seconde matrice intermédiaire étant cohérents ; et à exécuter une opération de convolution sur la première matrice intermédiaire au moyen de la seconde matrice intermédiaire, et à générer un résultat de convolution. Dans le procédé, l'exécution de l'opération de convolution sur la première matrice intermédiaire au moyen de la seconde matrice intermédiaire est équivalente à l'exécution d'une opération de convolution sur la matrice de données d'échantillon au moyen de la matrice de noyau de convolution ; et la quantité de données d'une convolution entre les deux matrices dans une unité de temps peut être augmentée, ce qui permet d'assurer relativement l'efficacité globale d'un processus d'opération de convolution. La présente invention concerne en outre un appareil et un dispositif de convolution, ainsi qu'un support de stockage, dont les effets avantageux sont identiques à ceux susmentionnés.
PCT/CN2020/087105 2020-01-20 2020-04-27 Procédé, appareil et dispositif d'opération de convolution, et support de stockage WO2021147196A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010065274.4 2020-01-20
CN202010065274.4A CN111310891A (zh) 2020-01-20 2020-01-20 一种卷积运算方法、装置、设备及存储介质

Publications (1)

Publication Number Publication Date
WO2021147196A1 true WO2021147196A1 (fr) 2021-07-29

Family

ID=71146891

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/087105 WO2021147196A1 (fr) 2020-01-20 2020-04-27 Procédé, appareil et dispositif d'opération de convolution, et support de stockage

Country Status (2)

Country Link
CN (1) CN111310891A (fr)
WO (1) WO2021147196A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111882029A (zh) * 2020-06-22 2020-11-03 华控清交信息科技(北京)有限公司 一种数据处理方法及装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017049496A1 (fr) * 2015-09-23 2017-03-30 Intel Corporation Appareil et procédé destinés à une quantification locale pour des réseaux neuronaux convolutionnels (cnn)
CN107430537A (zh) * 2015-03-27 2017-12-01 英特尔公司 从片上dram ecc中提取选择性信息
CN108122030A (zh) * 2016-11-30 2018-06-05 华为技术有限公司 一种卷积神经网络的运算方法、装置和服务器

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108171327A (zh) * 2017-12-25 2018-06-15 郑州云海信息技术有限公司 一种基于卷积运算的矩阵转化方法、装置及介质
CN109871510B (zh) * 2019-01-08 2024-01-23 广东浪潮大数据研究有限公司 二维卷积运算处理方法、系统、设备及计算机存储介质

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107430537A (zh) * 2015-03-27 2017-12-01 英特尔公司 从片上dram ecc中提取选择性信息
WO2017049496A1 (fr) * 2015-09-23 2017-03-30 Intel Corporation Appareil et procédé destinés à une quantification locale pour des réseaux neuronaux convolutionnels (cnn)
CN108122030A (zh) * 2016-11-30 2018-06-05 华为技术有限公司 一种卷积神经网络的运算方法、装置和服务器

Also Published As

Publication number Publication date
CN111310891A (zh) 2020-06-19

Similar Documents

Publication Publication Date Title
US11403838B2 (en) Image processing method, apparatus, equipment, and storage medium to obtain target image features
US10769749B2 (en) Processor, information processing apparatus, and operation method of processor
EP3637281A1 (fr) Accélérateur opérationnel
CN112801228B (zh) 一种文本识别方法、电子设备及其存储介质
CN109784372B (zh) 一种基于卷积神经网络的目标分类方法
WO2018214769A1 (fr) Procédé, dispositif, et système de traitement d'image
CN111199516B (zh) 基于图像生成网络模型的图像处理方法、系统及存储介质
WO2019076109A1 (fr) Procédé et dispositif de regroupement d'informations d'image, support d'informations et processeur
CN111899185A (zh) 图像降噪模型的训练方法、装置、电子设备和存储介质
US20210034900A1 (en) Method and apparatus for extracting image data in parallel from multiple convolution windows, device, and computer-readable storage medium
WO2021147196A1 (fr) Procédé, appareil et dispositif d'opération de convolution, et support de stockage
JP2017532655A (ja) カスケーディングスタイルシートファイルの圧縮
CN113498521A (zh) 文本检测方法及装置、存储介质
CN117252890A (zh) 颈动脉斑块分割方法、装置、设备及介质
CN111144407A (zh) 一种目标检测方法、系统、装置及可读存储介质
CN109145313A (zh) 语句的翻译方法、装置和存储介质
CN110059563B (zh) 一种文本处理方法和装置
CN110276332B (zh) 一种视频特征处理方法及装置
CN113496228B (zh) 一种基于Res2Net、TransUNet和协同注意力的人体语义分割方法
US20240071066A1 (en) Object recognition method and apparatus, and device and medium
CN111542837A (zh) 三维卷积神经网络计算装置及相关产品
CN107862316A (zh) 卷积运算方法及装置
CN112766471B (zh) 运算装置及相关产品
CN110032564B (zh) 一种数据表关联关系的确定方法和装置
CN114663320A (zh) 图像处理方法和数据集扩充方法、存储介质和电子设备

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20916109

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20916109

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 20916109

Country of ref document: EP

Kind code of ref document: A1