Embedded system-oriented neural network mapping method and device

An embedded system and neural network technology, applied in the field of neural network systems and neural network mapping devices, can solve problems such as manpower and time consumption, affecting product prototype verification and time to market, and it is difficult to efficiently apply embedded platforms. Maximize performance and speed up implementation
CN107958285AInactive Publication Date: 2018-04-24深圳普思英察科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
深圳普思英察科技有限公司
Publication Date
2018-04-24
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses an embedded system-oriented neural network mapping method and device. The method comprises the following steps of: establishing a neural network structure and obtaining parameters of the neural network; generating a data flow chart for describing the neural network according to the neural network structure and the parameter of the neural network; obtaining a deep learning calculation unit library and realizing the data flow chart of the neural network by utilizing the deep learning calculation unit library so as to obtain a realization program of the neural network. According to the method and device, the neural network can be mapped into the realization program applied to an embedded system by taking the deep learning calculation unit, so that the realization speed, on an embedded platform, of the neural network can be improved and performance of the embedded platform can be maximized.
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Description

technical field

[0001] The invention relates to the technical field of machine learning, in particular to a neural network-oriented mapping method for embedded systems, a neural network system for embedded systems, and a neural network-oriented mapping device for embedded systems. Background technique

[0002] In recent years, deep learning algorithms have been successfully applied in fields such as image search and language recognition. Due to the large amount of calculation, high memory usage and real-time application requirements, deep learning algorithms are often deployed in the cloud. With the advancement of chip technology and architecture technology, as well as the advent of lightweight deep learning models, deep learning algorithms can already be implemented in smartphones and embedded devices, and will become a new artificial intelligence technology for intelligent robots, drones and unmanned vehicles. The basic functional unit of an intelligent application. [0...

Claims

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