Neural network training method and related products
A neural network and reverse technology, applied in the field of neural networks, can solve the problems of high power consumption and large amount of calculation, and achieve the effect of low power consumption, small amount of calculation, saving transmission resources and computing resources
Active Publication Date: 2020-04-14
CAMBRICON TECH CO LTD
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Problems solved by technology
The operation of the existing neural network is based on CPU (Central Processing Unit, central processing unit) or GPU (English: Graphics Processing Unit, graphics processing unit) to realize the forward operation of neural network, the calculation amount of this kind of forward operation is large, high power consumption
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[0044] In order to enable those skilled in the art to better understand the solutions of the disclosure, the technical solutions in the embodiments of the disclosure will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the disclosure. Obviously, the described embodiments are only It is a part of the embodiments of this disclosure, but not all the embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of this disclosure.
[0045] In the method provided in the first aspect, the step of determining the nth output result gradient, the nth layer input data, and the nth layer of weight group data corresponding to the nth reverse data type according to the nth reverse operation complexity, include:
[0046] The nth reverse operation complexity is compared with a preset threshold. If the nth reverse operation...
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The present disclosure provides a neural network training method and related products executed on an integrated circuit chip device. The neural network includes multiple layers. The method includes the following steps: receiving a training instruction, and determining the input data of the first layer and the first layer according to the training instruction. The weight data of the first layer, the calculation device executes the n-layer forward operation of the neural network through the input data of the first layer and the weight data of the first layer to obtain the nth output result; obtain the nth output result gradient according to the nth output result, according to The training instruction obtains the nth reverse operation of the nth layer reverse operation, and the calculation device obtains the nth reverse operation according to the nth output result gradient, the nth layer input data, the nth layer weight group data and the nth reverse operation towards operational complexity. The technical solution provided by the present disclosure has the advantages of small calculation amount and low power consumption.
Description
Technical field [0001] This disclosure relates to the field of neural networks, and in particular to a neural network training method and related products. Background technique [0002] Artificial Neural Network (ANN) is a research hotspot that has emerged in the field of artificial intelligence since the 1980s. It abstracts the human brain neuron network from the perspective of information processing, establishes a simple model, and composes different networks according to different connection methods. In engineering and academia, it is often referred to as neural network or quasi-neural network. A neural network is a computing model, which is composed of a large number of nodes (or neurons) interconnected. The calculation of the existing neural network is based on CPU (Central Processing Unit, central processing unit) or GPU (English: Graphics Processing Unit, graphics processing unit) to realize the forward calculation of the neural network. This kind of forward calculation ...
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IPC IPC(8): G06N3/063
CPCG06N3/063Y02D10/00
Inventor 不公告发明人
Owner CAMBRICON TECH CO LTD