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公开(公告)号：US12223271B2
公开(公告)日：2025-02-11
申请号：US17874394
申请日：2022-07-27
Inventor： Zeyu Chen , Haifeng Wang , Tian Wu , Dianhai Yu , Yanjun Ma , Xiaoguang Hu
IPC: G06F40/10 , G06F40/284 , G06F40/47
Abstract: Provided are a text processing method, a device and a storage medium, relating to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing text processing on first text, by using a text processing acceleration operator; and processing, in parallel and faster, content after the text processing, by using the text processing acceleration operator. Text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.
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公开(公告)号：US11929871B2
公开(公告)日：2024-03-12
申请号：US17718149
申请日：2022-04-11
Inventor： Cheng Cui , Tingquan Gao , Shengyu Wei , Yuning Du , Ruoyu Guo , Bin Lu , Ying Zhou , Xueying Lyu , Qiwen Liu , Xiaoguang Hu , Dianhai Yu , Yanjun Ma
IPC: G06K9/62 , G06F18/214 , H04L41/0806 , H04L41/084 , H04L41/0894
CPC classification number: H04L41/0806 , G06F18/214 , H04L41/0846 , H04L41/0894
Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.
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公开(公告)号：US11531529B2
公开(公告)日：2022-12-20
申请号：US17500779
申请日：2021-10-13
Inventor： Liujie Zhang , Xiang Lan , Huihuang Zheng , Hongyu Liu , Wei Zhou , Yanjun Ma , Dianhai Yu , Haifeng Wang
Abstract: The present disclosure discloses a method, an apparatus and an electronic device for deploying an operator in a deep learning framework and relates to the field of artificial intelligence technology such as deep learning. And the solution is: acquiring a source file of the operator; compiling the source file of the operator to form a dynamic link library of the operator; generating an interface file transferred from the dynamic link library of the operator; generating an installable library file according to the dynamic link library and the interface file; installing the installable library file to a target programming language library.
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公开(公告)号：US12032477B2
公开(公告)日：2024-07-09
申请号：US17856091
申请日：2022-07-01
Inventor： Tian Wu , Yanjun Ma , Dianhai Yu , Yehua Yang , Yuning Du
CPC classification number: G06F11/3688 , G06N3/08
Abstract: A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.
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公开(公告)号：US11983086B2
公开(公告)日：2024-05-14
申请号：US17989644
申请日：2022-11-17
Inventor： Haifeng Wang , Xiaoguang Hu , Dianhai Yu , Xiang Lan , Yanjun Ma
CPC classification number: G06F11/3409 , G06N3/063
Abstract: The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.
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Patent Agency Ranking
公开(公告)号：US20220036241A1
公开(公告)日：2022-02-03
申请号：US17501003
申请日：2021-10-14
Inventor： Tianjian He , Dianhai Yu , Zhihua Wu , Daxiang Dong , Yanjun Ma
Abstract: The present disclosure discloses a method, an apparatus and a storage medium for training a deep learning framework, and relates to the artificial intelligence field such as deep learning and big data processing. The specific implementation solution is: acquiring at least one task node in a current task node cluster, that meets a preset opening condition when a target task meets a training start condition; judging whether a number of nodes of the at least one task node is greater than or equal to a preset number; synchronously training the deep learning framework of the target task by the at least one task node according to sample data if the number of nodes is greater than the preset number; and acquiring a synchronously trained target deep learning framework when the target task meets a training completion condition.
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公开(公告)号：US20230186024A1
公开(公告)日：2023-06-15
申请号：US17874394
申请日：2022-07-27
Inventor： Zeyu Chen , Haifeng Wang , Tian Wu , Dianhai Yu , Yanjun Ma , Xiaoguang Hu
IPC: G06F40/284 , G06F40/47
CPC classification number: G06F40/284 , G06F40/47
Abstract: Provided are a text processing method, a device and a storage medium, relating to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing text processing on first text, by using a text processing acceleration operator; and processing, in parallel and faster, content after the text processing, by using the text processing acceleration operator. Text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.
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8.发明授权公开(公告)号：US11604774B2
公开(公告)日：2023-03-14
申请号：US17480294
申请日：2021-09-21
Inventor： Liujie Zhang , Yamei Li , Huihuang Zheng , Hongyu Liu , Xiang Lan , Dianhai Yu , Yanjun Ma , Tian Wu , Haifeng Wang
Abstract: A method and apparatus of converting a schema in a deep learning framework, an electronic device, and a computer storage medium are provided. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema.
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公开(公告)号：US12118770B2
公开(公告)日：2024-10-15
申请号：US17657118
申请日：2022-03-29
Inventor： Shengyu Wei , Yuning Du , Xueying Lyu , Ying Zhou , Qiao Zhao , Qiwen Liu , Ran Bi , Xiaoguang Hu , Dianhai Yu , Yanjun Ma
IPC: G06V10/00 , G06V10/40 , G06V10/74 , G06V10/764
CPC classification number: G06V10/764 , G06V10/40 , G06V10/761
Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.
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10.发明公开公开(公告)号：US20230206024A1
公开(公告)日：2023-06-29
申请号：US17891617
申请日：2022-08-19
Inventor： Ji Liu , Zhihua Wu , Danlei Feng , Chendi Zhou , Minxu Zhang , Xinxuan Wu , Xuefeng Yao , Dejing Dou , Dianhai Yu , Yanjun Ma
CPC classification number: G06N3/04 , G06F11/3409
Abstract: A resource allocation method, including: determining a neural network model to be allocated resources, and determining a set of devices capable of providing resources for the neural network model; determining, based on the set of devices and the neural network model, first set of evaluation points including first number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme; updating and iterating first set of evaluation points to obtain second set of evaluation points including second number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme, and second number being greater than first number; and selecting a resource allocation scheme with minimum resource use cost from the second set of evaluation points as a resource allocation scheme for allocating resources to the neural network model.
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