Hybrid precision deep learning algorithm

A deep learning and algorithm technology, applied in neural learning methods, biological neural network models, etc., can solve problems such as different accuracy, and achieve the effect of improving computing speed

Inactive Publication Date: 2017-05-10
DAWNING INFORMATION IND BEIJING
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there are double-precision deep learning and single-precision deep

Method used

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Embodiment Construction

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, rather than all embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work, all belong to the protection scope of the present invention .

[0020] image 3 It is a schematic flow chart of the mixed-precision deep learning algorithm provided by Embodiment 1 of the present invention, such as image 3 As shown, the mixed precision deep learning algorithm includes the following steps:

[0021] S101. Use a single-precision many-core processor to perform forward propagation calcula...

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Abstract

The invention relates to the technical field of deep learning algorithms and specifically relates to a hybrid precision deep learning algorithm. The to-be-solved technical problem is ensuring calculation precision and improving calculation efficiency at the same time. The technical scheme includes steps of: S101, utilizing a single-precision many-core processor for forward propagation calculation and calculating the value of each nerve cell for each network layer; S102, utilizing the single-precision many-core processor for backward propagation calculation and calculating error residual values for each network layer; S103, utilizing the single-precision many-core processor for calculating weight increment; S104, updating the weight increment calculated by the single-precision many-core processor to weight increment calculated by a high-precision many-core processor and implementing calculation of an irritation. The invention is suitable for the deep leaning field.

Description

technical field [0001] The invention relates to the technical field of deep learning algorithms, in particular to a mixed precision deep learning algorithm. Background technique [0002] Deep learning is a new field in machine learning research. Its motivation is to establish and simulate the neural network of human brain for analysis and learning. It imitates the mechanism of human brain to explain data, such as images, sounds and texts. Its concept is proposed by Hinton et al. was proposed in 2006. Based on the deep belief network (DBN), a non-supervised greedy layer-by-layer training algorithm is proposed, which brings hope to solve the optimization problems related to the deep structure, and then a multi-layer autoencoder deep structure is proposed. In addition, the convolutional neural network proposed by Lecun et al. is the first true multi-layer structure learning algorithm, which uses spatial relative relationships to reduce the number of parameters to improve train...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/08
Inventor 许建卫刘立窦晓光
Owner DAWNING INFORMATION IND BEIJING
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