CNN (convolutional neural network) construction method and system

Abstract

The invention discloses a CNN (convolutional neural network) construction method. The CNN construction method comprises the following steps: a unitization instruction is received, according to the unitization instruction, hardware resources for convolutional operation are configured into a convolutional unit, hardware resources for activating operation are configured into an activating unit, and hardware resources for pooling operation are configured into a pooling unit; a configuration file is read and contains convolutional unit configuration parameters, activating unit configuration parameters, pooling unit configuration parameters, the number of convolutional layers and the number of full-connection layers; the convolutional unit is configured according to the convolutional unit configuration parameters, the activating unit is configured according to the activating unit configuration parameters, the pooling unit is configured according to the pooling unit configuration parameters, and the number of the convolutional layers and the number of the full-connection layers are configured, so that the CNN is constructed. The invention further discloses a CNN construction system. The CNN construction method is high in construction efficiency.

Description

technical field [0001] The present invention relates to the technical fields of Machine Learning (ML) and Artificial Intelligence (AI), in particular to a method and system for constructing a Convolutional Neural Network (CNN). Background technique [0002] Deep Learning (DL) is a method of simulating the way of thinking of the human brain and dealing with problems. The number of computing neurons in the human brain is on the order of tens of billions, and even a "small" CNN requires huge calculations, and almost all deep learning networks run on CPUs (or CPU clusters), or GPUs (or GPU cluster) hardware platform, the required hardware resources are very huge, resulting in high cost and power consumption, and slow running speed. Many CNNs can only achieve a few frames per second when running on a high-performance chip platform. speed, no real-time processing is possible. [0003] The convolutional neural network includes a convolutional layer and a fully connected layer. It...

AI Technical Summary

Problems solved by technology

The number of computing neurons in the human brain is on the order of tens of billions, and even a "small" CNN requires huge calculations, and almost all deep learning networks run on CPUs (or CPU clusters), or GPUs (or GPU cluster) hardware platform, the required hardware resources are very huge, resulting in high cost and power consumption, and slow running speed. Many CNNs can only achieve a few frames per second when running on a high-performance chip platform. speed, no real-time processing

[0003] The convolutional neural network includes a convolutional layer and a fully connected layer. Its calculation process is calculated layer by layer. The calculation required is very large, and a specific convolutional neural network can only achieve specific functions. When constructing a new convolutional neural network When the neural network supports new functions, it cannot directly make configuration changes to the previous convolutional neural network to build a new convolutional neural network

[0004] The disadvantage of the above technical solution is that the construction process of the convolutional neural network is inefficient

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