Research On Deep Learning Algorithm Based On Ultra-high Performance Autonomous Microprocessor

With the rapid development of artificial intelligence in recent years,deep learning,the most important technology in current artificial intelligence theory,has been widely used in various fields and has played a very important role.Among them,computer vision has always been one of the most active research directions in deep learning research,and target detection is the focus and difficulty of computer vision research.In the military field,target detection plays an important role in battlefield reconnaissance,automatic patrol strikes,unmanned operations,and defensive vigilance.Because deep learning involves a lot of vector and multi-matrix operations,a large amount of research on deep learning is currently based on GPUs.As a general-purpose computing platform,there is a problem of high power consumption in application deployment.This paper mainly studies the application of a deep learning algorithm without GPU.According to the characteristics of the FT series DSP independently developed by the School of Computer Science and Technology of National University of Defense Technology,combined with the analysis of existing deep convolutional neural networks,a deep target for mobile military targets is developed.Learning method target detection system,and the deployment and optimization have been completed on the FT-M7002 multi-core DSP embedded platform,realizing a low-power and high-performance embedded deep learning application.The main work of this article is as follows:(1)A small sample data set for military target detection is established.In the early stage of deep learning algorithms,a large amount of data is required for repeated iterative training.Due to the special nature of military targets,there are currently no publicly available data sets that can be used directly.A total of 3,600 images of military targets in six categories are collected in this paper,and the data are labeled.This data set can be used as a basic data set for deep learning and computer vision related research training.(2)Improve and improve on the basis of existing target detection algorithms.This article analyzes and summarizes the current deep learning target detection algorithms,and combines advanced YOLO and Faster R-CNN ideas to achieve a target detection network that is more suitable for FT series DSP deployment.In the end,it achieved a correct recognition rate of 75% on the training set,which is higher than 67% of YOLO and 72% of Faster R-CNN.Generally,it can reach a recognition rate of more than 80% in scene measurement.(3)A small sample of deep learning training is implemented.This paper combines transfer learning and other technologies to implement a small sample-based deep learning network and successfully deploy it in the FT-M7002 DSP IMG image system,providing a reference method for small sample deep learning application research.(4)The deep learning target detection system is optimized on the FT series embedded platform.One of the major difficulties in the implementation of deep learning applications is that the huge amount of calculations leads to high demand for computing power,and the power consumption and cost of corresponding general-purpose computing platforms are difficult to control.Based on the architecture characteristics of FT series DSP,this paper deeply optimizes the operation process of deep convolutional neural network,completes the military target detection task on the FT-M7002 multi-core DSP embedded platform,and makes full use of the embedded platform’s The advantages of power consumption and ease of embedding in other equipment provide a reference method for the application of deep learning algorithms on FT series DSPs,and have high practical value.Compared with the advanced desktop-level CPU intel i7-4900 and the Nvdia GPU GTX 1080 Ti commonly used in deep learning training,the power consumption of the DSP system designed in this paper is only 17.8% and 6.1%,respectively,under the same computing load on the same network..