Scale-extensible convolutional neural network acceleration system and method

A convolutional neural network and acceleration system technology, applied in biological neural network model, neural architecture, physical implementation, etc., can solve the problems of partial calculation of network that does not support updating, poor flexibility, large overhead design time, etc., to achieve good flexibility performance, reduced data transfer, good versatility and scalability

Active Publication Date: 2020-06-05
TSINGHUA UNIV
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Problems solved by technology

If you need to expand, you need to modify and design the corresponding system, so it brings a lot of overhead and extra design time
And if it is a separate convolution

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  • Scale-extensible convolutional neural network acceleration system and method
  • Scale-extensible convolutional neural network acceleration system and method
  • Scale-extensible convolutional neural network acceleration system and method

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Embodiment Construction

[0031] The implementation of the present invention will be described in detail below in conjunction with the drawings and examples.

[0032] As the convolutional neural network continues to deepen, the amount of convolution calculations is increasing, but because convolution has certain rules, parallel computing can be achieved. The activation function can also be accelerated by a dedicated circuit, and the supported activation function modules are mainly ReLU and Leaky ReLU functions.

[0033]Based on this, the present invention provides a scalable convolutional neural network acceleration system, which mainly includes modules such as a processor and a convolutional acceleration core. There is at least one convolutional acceleration core. When expanding the scale, only need to increase the convolutional acceleration core The number of programs running on the processor is modified, and other hardware modules do not need to be changed. That is, multiple convolution acceleratio...

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Abstract

A scale-extensible convolutional neural network acceleration system, and the system comprises a processor and at least one convolutional acceleration core, wherein each convolutional acceleration coremainly comprises a computing array, a controller and an on-chip cache; when the scale is extended, only the number of the convolutional acceleration cores needs to be increased, programs running on the processor need to be modified, and other hardware modules do not need to be changed. Namely, a plurality of convolution acceleration kernels can be added to improve the scale and computing performance of the system. The invention further provides a method based on the scale-extensible convolutional neural network acceleration system, extra overhead caused by scale extension can be reduced to agreat extent, and therefore the scale-extensible convolutional neural network acceleration system can be deployed on different hardware platforms. Meanwhile, the software and hardware cooperation modeis good in universality, and different convolutional neural networks can be supported. Compared with other circuits, the method has universality and expandability.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, relates to the improvement of neural network computing performance, and in particular to a scalable convolutional neural network acceleration system and method. Background technique [0002] In recent years, convolutional neural networks have been widely deployed in IoT smart terminals, autonomous driving, and data centers due to their superior performance. Relying on a large amount of training data, network algorithms can be applied to many fields such as image classification and detection, human-computer games, and natural language processing. [0003] However, as the convolutional neural network structure becomes more complex and its depth continues to increase, the amount of network calculations also increases, resulting in problems such as low computing efficiency or high power consumption on general-purpose hardware platforms. Computational neural networks. The design of c...

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Application Information

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IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045Y02D10/00
Inventor 何虎赵烁
Owner TSINGHUA UNIV
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