Sample feature processing method and device, electronic equipment and storage medium
A technology of sample characteristics and processing methods, applied in the direction of reasoning methods, neural learning methods, biological neural network models, etc., can solve the problems of slow reasoning speed of parameter scale, difficult deep learning algorithm model, low processing efficiency, etc., to improve the operation speed , reduce computing resources, and shorten the time
Pending Publication Date: 2021-10-26
BEIJING XIAOMI MOBILE SOFTWARE CO LTD +1
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However, the deep learning algorithm model shows a trend of becoming larger and deeper, accompanied by larger parameter scale and slower reasoning speed
[0003] In related technologies, the development of deep learning algorithm models will bring huge challenges to the operation of electronic devices, especially mobile terminal devices. Due to the limitation of hardware resources of mobile terminal devices, it is difficult for mobile terminal devices to directly run large deep learning algorithm models. , running slowly and with low processing efficiency
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[0045] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.
[0046] In order to facilitate the understanding of those skilled in the art, the embodiments of the present disclosure list multiple implementations to clearly illustrate the technical solutions of the embodiments of the present disclosure. Of course, those skilled in the art can understand that the multiple embodiments provided by the embodiments of the present disclosure can be executed indepe...
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The embodiment of the invention discloses a sample feature processing method, and the method comprises the steps of obtaining first reference data in the input data of an activation function in a neural network model, wherein the input data comprises feature data of sample features of the activation function to be input; inputting the input data into the activation function to obtain output data of the activation function; based on the first reference data, determining second reference data corresponding to the first reference data in the output data; and quantizing the output data based on the second reference data. Compared with a mode of obtaining the second reference data from the output data after the output data is obtained based on the first reference data, the time for determining the second reference data is shorter, and the efficiency is higher. And the output data is quantized based on the second reference data, so that the operation speed can be improved, and consumed operation resources can be reduced.
Description
technical field [0001] The present disclosure relates to the technical field of wireless communication but is not limited to the technical field of wireless communication, and in particular relates to a method, device, electronic device and storage medium for processing sample characteristics. Background technique [0002] With the development of computer technology, deep learning has been widely used in many fields such as computer vision processing, natural language processing and speech processing. However, the deep learning algorithm model shows a trend of becoming larger and deeper, accompanied by larger parameter scale and slower reasoning speed. [0003] In related technologies, the development of deep learning algorithm models will bring huge challenges to the operation of electronic devices, especially mobile terminal devices. Due to the limitation of hardware resources of mobile terminal devices, it is difficult for mobile terminal devices to directly run large dee...
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IPC IPC(8): G06N3/04G06N3/08G06N5/04
CPCG06N3/04G06N3/08G06N5/04
Inventor 吴晓琳
Owner BEIJING XIAOMI MOBILE SOFTWARE CO LTD