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Binary convolutional device and corresponding binary convolutional neural network processor

A binary convolution neural and convolution technology, applied in the processor field of neural network model calculation, can solve the problems of resource waste and non-existence, achieve the effect of reducing storage capacity and energy consumption, and improving computing efficiency

Active Publication Date: 2017-09-26
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

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  • Binary convolutional device and corresponding binary convolutional neural network processor
  • Binary convolutional device and corresponding binary convolutional neural network processor
  • Binary convolutional device and corresponding binary convolutional neural network processor

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Embodiment Construction

[0037] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] 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.

[0039] 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 present invention provides a binary convolutional device and a corresponding binary convolutional neural network processor. The binary convolutional device comprises: an XNOR gate taking elements in an employed convolution kernel and corresponding elements in data to be subjected to convolution as input, wherein the elements in the employed convolution kernel and the corresponding elements in the data to be subjected to convolution are both binary forms; and an accumulation device taking the output of the XNOR gate as input and configured to perform accumulation of the output of the XNOR gate to output a binary convolutional result. According to the technical scheme, the bit wide of data for calculation is reduced in the operation process so as to reach the effect of improving operation efficiency and reduce storage capacity and energy consumption.

Description

technical field [0001] The present invention relates to computer processors, in particular to processors for neural network model calculations. 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, i...

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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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