Convolutional neural network hardware module deployment method

A convolutional neural network and hardware module technology, applied in biological neural network models, neural architecture, physical realization, etc., can solve problems such as low recognition speed and low model operation efficiency

Inactive Publication Date: 2018-08-17
JINAN INSPUR HIGH TECH TECH DEV CO LTD
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Problems solved by technology

On the application side, due to resource constraints, it is often only possible to run a small model, and

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  • Convolutional neural network hardware module deployment method
  • Convolutional neural network hardware module deployment method

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Embodiment Construction

[0019] The present invention provides a convolutional neural network hardware module deployment method:

[0020] The upper-layer compiler of the convolutional neural network simulates and compares the implementation forms of the convolutional neural network according to the target hardware resources and the data volume of the convolutional neural network model, weighs the hardware resources and the speed requirements of the convolutional neural network, and determines the convolutional neural network. The deployment parameters of each hardware module of the neural network, divide the number of each hardware module and determine the connection mode between hardware modules, so as to realize the deployment of convolutional neural network hardware modules.

[0021] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0022] Among the various hardware modules of the convolutional neural network in the present inv...

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Abstract

The invention discloses a convolutional neural network hardware module deployment method, and relates to the field of convolutional neural network implementation. The method comprises the steps that an upper layer compiler of a convolutional neural network is used to perform simulation comparison on the implementation form of the convolutional neural network according to a target hardware resourceand a data volume of a convolutional neural network model; the requirements of the hardware resource and convolutional neural network speed are weighed to determine a deployment parameter of each hardware module of the convolutional neural network; the number of each hardware module is divided, and the connection mode among hardware modules is determined, so that the deployment of the convolutional neural network hardware modules is achieved.

Description

technical field [0001] The invention discloses a convolutional neural network hardware module deployment method, and relates to the field of convolutional neural network implementation. Background technique [0002] Convolutional neural network (CNN) is a variant model of multi-layer perceptron (MLP). It evolved from the biological concept that the visual cortex cells cover the entire visual domain in a certain way, like some filters, they are locally sensitive to the input image, so they can better mine natural images. The spatial relationship information of the target. CNN mines the spatial local association information of objects of interest in natural images by strengthening the local connection patterns of nodes between adjacent layers in the neural network. [0003] Most of CNN's deployment methods currently use the X86 architecture CPU platform + GPU as the hardware environment, run software frameworks such as TensorFlow, Caffe, etc. in the real-time operating syste...

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Application Information

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IPC IPC(8): G06N3/063G06N3/04
CPCG06N3/063G06N3/045
Inventor 王子彤姜凯聂林川
Owner JINAN INSPUR HIGH TECH TECH DEV CO LTD
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