Neural network model training method and device
A technology of neural network model and training method, applied in the field of neural network model training method and device, capable of solving problems such as high hardware resource overhead, high single-precision floating-point data, and large data volume
Pending Publication Date: 2021-03-05
HANGZHOU HIKVISION DIGITAL TECH
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However, due to the high bit width of single-precision floating-point data, the amount of data involved in the operation is large, resulting in a high hardware resource overhead for running the neural network model
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[0074] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0075] In order to reduce the hardware resource overhead required for running a neural network model, embodiments of the present application provide a neural network model training method, device, computer equipment, and machine-readable storage medium. In the following, the neural network model training method provided by the embodiment of the present application is firstly introduced.
[0076] The execution subject of a neural network training method provided in the embodiment of ...
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The embodiment of the invention provides a neural network model training method and device. The method comprises the steps: obtaining a training sample, and carrying out the training of a neural network model through the training sample. When neural network model training is carried out, integer fixed-point coding is carried out on the first activation quantity input into each network layer and the network weight of each network layer, the coded first activation quantity and network weight are integer fixed-point data with specified bit width, and when operation is carried out, integer fixed-point coding is carried out on the first activation quantity and network weight of each network layer; related matrix multiplication, matrix addition and other operations all adopt an integer fixed-point format, and the bit width of integer fixed-point data is obviously smaller than that of single-precision floating-point data, so that the hardware resource overhead required for operating a neuralnetwork model can be greatly reduced.
Description
technical field [0001] The present application relates to the technical field of machine learning, in particular to a neural network model training method and device. Background technique [0002] As an emerging field in machine learning research, deep neural network parses data by imitating the mechanism of the human brain. It is an intelligent model that analyzes and learns by establishing and simulating the human brain. At present, deep neural networks, such as convolutional neural networks, recurrent neural networks, and long-term short-term memory networks, have been well applied in target detection and segmentation, behavior detection and recognition, and speech recognition. [0003] At present, the training of the neural network model usually uses single-precision floating-point data for operations to ensure the accuracy of the convergence of the neural network model. However, due to the high bit width of single-precision floating-point data, the amount of data invol...
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Login to View More IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06N3/04G06N3/08
Inventor 张渊谢迪浦世亮
Owner HANGZHOU HIKVISION DIGITAL TECH