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Neural network accelerator

A neural network and accelerator technology, applied in the field of neural network, can solve problems such as difficulty in providing computing power, high power consumption, and application limitations

Pending Publication Date: 2020-10-09
SHENZHEN CORERAIN TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the improvement of the accuracy of convolutional neural network is accompanied by the rapid increase of computing cost and storage cost
It is difficult to provide enough computing power using multi-core central processing units (CPUs)
Although the graphics processing unit (GPU) can process complex convolutional neural network models at high speed, the power consumption is too high and its application in embedded systems is limited

Method used

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Experimental program
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Effect test

Embodiment 1

[0031] figure 1 It is a schematic structural diagram of a neural network accelerator provided in Embodiment 1 of the present invention, which is applicable to the calculation of neural networks. Such as figure 1 As shown, the neural network accelerator provided by Embodiment 1 of the present invention includes: a storage module 100 , a convolution calculation module 200 , a first control module 300 and a tail calculation module 400 . The convolution calculation module 200 is used to perform convolution operation on the input data of the preset neural network to obtain the first output data; the tail calculation module 400 is used to calculate the first output data to obtain the second output data; the storage module 100 is used to The input data and the second output data are buffered; the first control module 300 is configured to transmit the first output data to the tail calculation module.

[0032] Further, the convolution calculation module 200 includes a plurality of co...

Embodiment 2

[0038] figure 2 It is a schematic structural diagram of a neural network accelerator provided by Embodiment 2 of the present invention, and this embodiment is a further refinement of the foregoing embodiments. Such as figure 2 As shown, the neural network accelerator provided by Embodiment 2 of the present invention includes: a storage module 100 , a convolution calculation module 200 , a first control module 300 , a tail calculation module 400 and a second control module 500 . The convolution calculation module 200 is used to perform convolution operation on the input data of the preset neural network to obtain the first output data; the tail calculation module 400 is used to calculate the first output data to obtain the second output data; the storage module 100 is used to Cache the input data and the second output data; the first control module 300 is used to transmit the first output data output by the convolution calculation module 200 to the tail calculation module 40...

Embodiment 3

[0046] image 3 A schematic structural diagram of a neural network accelerator provided by Embodiment 3 of the present invention. This embodiment is a further refinement of the storage module and the convolution calculation unit in the foregoing embodiments. Such as image 3 As shown, the neural network accelerator provided by Embodiment 3 of the present invention includes: a storage module 100 , a convolution calculation module 200 , a first control module 300 , a tail calculation module 400 , a second control module 500 and a preset parameter configuration module 600 . The convolution calculation module 200 is used to perform convolution operation on the input data of the preset neural network to obtain the first output data; the tail calculation module 400 is used to calculate the first output data to obtain the second output data; the storage module 100 is used to Cache the input data and the second output data; the first control module 300 is used to transmit the first o...

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Abstract

The embodiment of the invention discloses a neural network accelerator comprising a convolution calculation module which is used for carrying out convolution operation of input data inputted into a preset neural network to obtain first output data, a tail calculation module which is used for calculating the first output data to obtain second output data, a storage module which is used for cachingthe input data and the second output data, and a first control module which is used for transmitting the first output data to the tail calculation module. The convolution calculation module comprisesa plurality of convolution calculation units, the tail calculation module comprises a plurality of tail calculation units, the first control module comprises a plurality of first control units, and atleast two convolution calculation units are connected with one tail calculation unit through one first control unit. According to the embodiment of the invention, the design of the tail calculation module in the neural network accelerator is optimized, and the resource consumption of the neural network accelerator is reduced.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of neural networks, and in particular to a neural network accelerator. Background technique [0002] In recent years, convolutional neural networks have developed rapidly and are widely used in computer vision and natural language processing. However, the improvement in the accuracy of convolutional neural networks is accompanied by a rapid increase in computational cost and storage cost. It is difficult to provide enough computing power using a multi-core central processing unit (CPU). Although the graphics processing unit (GPU) can process complex convolutional neural network models at high speed, the power consumption is too high, and its application in embedded systems is limited. [0003] Convolutional neural network accelerators based on FPGAs and ASICs, featuring high energy efficiency and massively parallel processing, have gradually become a hot research topic. Since the con...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/063G06N3/04G06F13/28
CPCG06N3/063G06F13/28G06N3/045G06N3/0464
Inventor 曾成龙李远超蔡权雄牛昕宇
Owner SHENZHEN CORERAIN TECH CO LTD