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Binary convolutional neural network processor and using method thereof

A binary convolution neural and network processor technology, applied in biological neural network models, physical realization, etc., can solve problems such as non-existence and waste of resources, achieve the effect of reducing storage capacity and energy consumption, and improving computing efficiency

Active Publication Date: 2017-09-12
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

However, there is no dedicated processor for binary convolutional neural networks
The bit width of the calculation unit of a general-purpose computer processor is usually multi-bit, and the calculation of the binary neural network will cause waste of resources

Method used

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  • Binary convolutional neural network processor and using method thereof
  • Binary convolutional neural network processor and using method thereof
  • Binary convolutional neural network processor and using method thereof

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Embodiment Construction

[0047] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0048] The neural network in computer science is a mathematical model imitating the synaptic connection structure in biology. The application system composed of neural network can realize many functions such as machine learning and pattern recognition.

[0049] The neural network is structurally divided into multiple layers, figure 1 A schematic diagram of a neural network multi-layer structure is shown. refer to figure 1 , the first layer in the multi-layer structure is an input layer, the last layer is an output layer, and the remaining layers are hidden layers. When using the neural network, the original image is input to the input layer, that is, the input layer layer, (the "image" in the present invention, "layer" refers to the original data to be processed, not only in a narrow sense The image obtained by taking photos), each layer i...

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Abstract

The invention provides a binary convolutional neural network processor which comprises a to-be-calculated data storage device used for storing elements of binary to-be-convolved data and binary convolution kernel elements, a binary convolution device used for performing binary convolution operation on the binary convolution kernel elements and the corresponding elements in the binary to-be-convolved data, a data scheduling device used for loading the convolution kernel elements and the corresponding elements in the to-be-convolved data into the binary convolution device, a pooling device used for pooling the convolution result, and a normalizing device used for normalizing the pooled result.

Description

technical field [0001] The invention relates to the storage and scheduling of data used in neural network model calculation. Background technique [0002] With the development of artificial intelligence technology, technologies involving deep neural networks, especially convolutional neural networks, have developed rapidly in recent years. In image recognition, speech recognition, natural language understanding, weather prediction, gene expression, content recommendation It has been widely used in fields such as artificial intelligence and intelligent robots. [0003] The deep neural network can be understood as an operation model, which contains a large number of data nodes, each data node is connected to other data nodes, and the connection relationship between each node is represented by weight. As deep neural networks continue to develop, so do their complexity. [0004] In order to balance the contradiction between complexity and operation effect, in the reference: Co...

Claims

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Application Information

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IPC IPC(8): G06N3/063
CPCG06N3/063
Inventor 韩银和许浩博王颖
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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