Heterogeneous fusion embedded intelligent computing implementation method

Abstract

The invention belongs to the field of embedded intelligent computing, and provides a heterogeneous fusion embedded intelligent computing implementation method. The method comprises the steps of determining algorithm characteristics of a required core intelligent algorithm by analyzing task characteristics of intelligent tasks in an equipment embedded system OODA full task chain, determining and selecting a core intelligent algorithm of a corresponding category according to the required algorithm characteristics, wherein the core intelligent algorithm is divided into a knowledge-driving algorithm, an intelligent optimization algorithm and a deep learning algorithm, scheduling through application service management, mapping the selected core intelligent algorithm to a corresponding softwareoperation framework in a system platform service layer, wherein all the software running frameworks are loaded into an adaptive hardware module when the system is started, and selecting a hardware module through resource service management to load the core intelligent algorithm. According to the method, efficient, flexible and universal intelligent computing support service can be provided for equipment OODA autonomous tasks, and execution of the equipment autonomous tasks is accelerated.

Description

Technical field [0001] The invention belongs to the field of embedded intelligent computing, and proposes an embedded intelligent computing implementation method. Background technique [0002] In recent years, with the emergence of new technologies and concepts such as big data, cloud computing, and deep learning, artificial intelligence has made significant progress in the field of perceptual intelligence and cognitive intelligence, which has triggered an intelligent revolution in the military field. The autonomous mission capability of equipment will become the key to success in equipment. Under the strong real-time, high dynamic and complex task environment in the future, it is necessary to equip embedded systems with real-time perception, cognition, decision-making and control capabilities during task execution. In the face of the ever-changing situation, the embedded system is continuously optimized. Own behavior, in a highly adaptive, flexible and autonomous way, executes ...

AI Technical Summary

Problems solved by technology

This will cause the equipment to face extremely high task complexity and application diversity in the process of autonomous tasks, and there are great differences in the calculation type and calculation complexity of different intelligent combat applications, which requires the equipment embedded system to provide more Embedded intelligent computing platform with high performance, higher flexibility and higher versatility

[0003] However, due to the limitations of the hardware platform and mission objectives of the traditional equipment embedded computing platform, on the one hand, it is mostly designed for general task management, and its ability to support intelligent tasks is relatively weak. The existing equipment embedded intelligent computing platform for intelligent combat The architecture can only support one or a few specific smart applications; on the other hand, there are relatively large restrictions in terms of architecture design, processing speed, power consumption, volume, etc., resulting in the timeliness of smart application running unable to meet high dynamic, strong confrontation, Requirements for timely response and rapid decision-making of equipment embedded systems in the case of strong electromagnetic interference

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