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Device, board card and method for processing neural network calculation and readable storage medium

A computing device and neural network model technology, applied in the field of methods, readable storage media, boards, and devices for processing neural network model calculations, can solve the problems of large calculation overhead, fast training speed, poor accuracy, etc., and achieve adaptability to hardware Configuration, the effect of reducing computational overhead

Pending Publication Date: 2022-05-03
ANHUI CAMBRICON INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Although the depth-separable convolution theoretically reduces the amount of parameters and can improve the calculation speed to a certain extent, in actual calculation, because the current hardware and software do not optimize the depth-separated convolution, although it has the advantage of low FLOPs, the training The speed is not necessarily fast, and the computational overhead is often greater than that of conventional convolution
Furthermore, depth-separable convolution does not take advantage of the information coupling of different channels, and its accuracy is worse than conventional convolution. Therefore, depth-separable convolution is not an ideal convolution choice for neural network calculations.

Method used

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  • Device, board card and method for processing neural network calculation and readable storage medium
  • Device, board card and method for processing neural network calculation and readable storage medium
  • Device, board card and method for processing neural network calculation and readable storage medium

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Embodiment Construction

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are part of the embodiments of the present disclosure, not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present disclosure.

[0024] It should be understood that the terms "first", "second", "third" and "fourth" in the claims, specification and drawings of the present disclosure are used to distinguish different objects, rather than to describe a specific order . The terms "comprising" and "comprises" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclud...

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Abstract

The invention relates to a device, a board card, a method and a readable storage medium for processing neural network model calculation, and the calculation device is included in an integrated circuit device, and the integrated circuit device comprises a universal interconnection interface and other processing devices. And the computing device interacts with other processing devices to jointly complete the computing operation specified by the user. The integrated circuit device can further comprise a storage device, and the storage device is connected with the computing device and the other processing devices and used for data storage of the computing device and the other processing devices.

Description

technical field [0001] The present disclosure relates generally to the field of neural networks. More specifically, the present disclosure relates to a device, a board, a method and a readable storage medium for processing neural network model calculation. Background technique [0002] Depthwise convolution and pointwise convolution are collectively called depthwise separable convolution. Its overall operation is similar to conventional convolution operations and can be used to extract features. Depth-separated convolution can significantly Reduce the dimension and calculation amount, separate the convolution point by point for inter-channel fusion or change the dimension. The academic community believes that compared with conventional convolution operations, it performs better in floating-point operations (FLOPs), so this structure is often used in some lightweight networks, such as the MobileNet model. [0003] Although the depth-separable convolution theoretically reduc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045
Inventor 不公告发明人
Owner ANHUI CAMBRICON INFORMATION TECH CO LTD