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Lightweight CNN model calculation accelerator based on FPGA

A model computing and accelerator technology, applied in the field of lightweight CNN model computing accelerators, can solve problems such as slow running speed of accelerators, achieve low power consumption, speed up computing speed, and improve computing efficiency

Active Publication Date: 2020-08-04
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is: for the problem that the accelerator in the prior art has slow running speed, propose a kind of lightweight CNN model calculation accelerator based on FPGA

Method used

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  • Lightweight CNN model calculation accelerator based on FPGA
  • Lightweight CNN model calculation accelerator based on FPGA
  • Lightweight CNN model calculation accelerator based on FPGA

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specific Embodiment approach 1

[0033] Specific implementation mode one: refer to figure 1 This embodiment is described in detail. The FPGA-based lightweight CNN model calculation accelerator described in this embodiment is characterized in that it includes: a weight buffer, a normalization layer, a convolution layer, a pooling layer, and a full connection. layer and Softmax classifier.

[0034] A feature of the present invention is that a layer fusion strategy is adopted to fuse and optimize adjacent convolution operations, batch normalization (Batch Norm, hereinafter referred to as BN) operations and activation operations in the neural network model, and use it as Independent functional units are merged into a unified convolutional layer.

[0035] Another feature of the present invention is that the PE unit in the convolutional layer is designed to be accelerated. Through the two steps of line buffer design and intra-layer pipeline strategy, the data is guaranteed to pass through in the form of data flow...

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Abstract

The invention discloses a lightweight CNN model calculation accelerator based on an FPGA, relates to the technical field of hardware acceleration, and aims to solve the problem of low operation speedof an accelerator in the prior art, and the lightweight CNN model calculation accelerator comprises a weight cache region, a normalization layer, a convolution layer, a pooling layer, a full connection layer and a Softmax classifier. According to the invention, the characteristics of fast parallel computing, low power consumption and high flexibility of FPGA are utilized; the CNN accelerator design for the lightweight network using the depth separable convolution structure is carried out, the neural network can be be deployed in a resource-limited occasion, the calculation efficiency of an algorithm is greatly improved, and the operation speed of the algorithm is increased.

Description

technical field [0001] The invention relates to the technical field of hardware acceleration, in particular to an FPGA-based lightweight CNN model computing accelerator. Background technique [0002] Convolutional Neural Networks (CNN) is a type of feed-forward neural network that includes convolution calculations and has a deep structure. It is one of the representative algorithms of deep learning and is currently widely used in image recognition, language recognition and other fields. Remarkable results have been achieved. But while the detection accuracy of CNN is getting higher and higher, its depth, size and corresponding computational complexity are also multiplying. At present, the size of the mainstream neural network model is often tens to hundreds of megabytes (Mbyte, or MB), which needs to store millions or even hundreds of millions of parameters, and perform hundreds of millions or even tens of billions of multiplication and addition operations. Such a large-sc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06F9/38G06F15/78G06N3/04
CPCG06N3/082G06F15/78G06F9/3867G06N3/045Y02D10/00
Inventor 彭宇姬森展马宁于希明彭喜元
Owner HARBIN INST OF TECH
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