Detection Algorithm Designing Based on Embedded Intelligent Platform
Recent years,with the development of deep learning,the need for designing detection algo-rithms suitable for embedded intelligent computing platforms has become increasingly urgent.This article introduces the design criteria for detection algorithms under embedded intelligent computing platforms.Firstly,a detailed analysis of the computing capabilities and resource limitations of the embedded platform is conducted to determine the constraints for algorithm design.Secondly,based on the requirements of the detection task,we should select an appropriate deep learning algorithm as the foundation and optimize the model based on the hardware characteristics of the NPU(Neural Processing Unit),so that the application algorithm can achieve better performance on the embedded platform.In addition,based on the embedded intelligent computing platform with Ascend 310 as the core,this article proposes optimization methods for specific computationally intensive operators,thereby improving the real-time performance of the algorithm on this hardware.Experimental results show that the method proposed in this article can achieve dual op-timization of accuracy and latency on embedded platforms,satisfying the needs of specific scenario appli-cations and providing a reference for the designing of detection algorithms under embedded intelligent computing platforms.
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维普
