Computing device and related products, computing method for executing artificial neural network model

Abstract

This application relates to a computing device and related products, and a computing method for executing an artificial neural network model. Compared with general processors and image processors, the computing device can improve the computing speed of self-learning computing, save computing time, and further reduce power consumption.

Description

technical field [0001] The present application relates to the technical field of artificial neural network, in particular to a computing device and related products, and a computing method for executing an artificial neural network model. Background technique [0002] Multi-layer artificial neural networks are widely used in the fields of pattern recognition, image processing, function approximation, and optimization calculations. In recent years, multi-layer artificial networks have been favored by academic circles and researchers due to their high recognition accuracy and good parallelism. industry is getting more and more attention. [0003] The self-learning operation of multi-layer artificial neural network is a very important link in the operation process of multi-layer artificial neural network. At present, the self-learning pre-training of multi-layer artificial neural networks adopts a layer-by-layer training method. For each layer, the pre-training is divided into...

AI Technical Summary

Problems solved by technology

Since the GPU is a device specially used to perform graphics and image calculations and scientific calculations, without special support for multi-layer artificial neural network operations, it still requires a lot of front-end decoding work to perform multi-layer artificial neural network operations, which brings a lot of problems. additional cost

In addition, the GPU only has a small on-chip cache, and the model data (weights) of the multi-layer artificial neural network need to be repeatedly moved from off-chip, and the off-chip bandwidth has become the main performance bottleneck.

In addition, the GPU has only a small on-chip cache, and the model data (weights) of the multi-layer artificial neural network need to be repeatedly moved from off-chip. The off-chip bandwidth has become the main performance bottleneck, and it has brought huge power consumption overhead.

[0013] Therefore, using the above-mentioned traditional technology to perform self-learning operations on multi-layer artificial neural networks, the power consumption overhead is relatively high

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