A GPU cluster deep learning edge computing system oriented to sensing information processing

Abstract

The invention relates to a GPU cluster deep learning edge computing system oriented to sensing information processing. pre-feature extraction is carried out on sensing information by using weak computing power of front-end intelligent sensing equipment; the quantity of original data information is greatly compressed, then the rest processing tasks are transmitted to a GPU cluster for large-scale sensing data feature clustering set processing, the computing power of front-end intelligent sensing equipment can be dynamically adapted through task splitting processing, and the cost pressure of theconsistency requirement of the front-end sensing equipment and hardware versions is reduced; The communication pressure of the edge computing network is reduced, so that the cost of constructing theedge computing network is greatly reduced; Network data feature transmission hides user privacy;, the SPMD advantages of the GPU are brought into play through the clustering operation according to thedata transmitted in the network and the stored data core characteristics, the parallel computing efficiency of edge computing is improved, and meanwhile, the large-scale parallel computing capacity of the GPU cluster and the advantages of low cost and high reliability are effectively brought into play.

Description

technical field [0001] The invention relates to a computer technology, in particular to a GPU cluster deep learning edge computing system oriented to sensor information processing. Background technique [0002] With the rapid development of Internet of Things technology and artificial intelligence technology, corresponding composite applications have been launched in various fields, especially the application of real-time analysis technology based on video has become a hot spot. For example, large-scale video surveillance is used for real-time analysis of public transportation system congestion, home care is used for elderly care in large communities, industrial automation sorting applications, etc. For these large-scale applications based on video sensing, many problems need to be solved: 1) The real-time processing of sensing information poses a challenge to the cost of front-end sensing equipment; 2) The real-time transmission of video data puts pressure on the communicat...

AI Technical Summary

Problems solved by technology

For these large-scale applications based on video sensing, many problems need to be solved: 1) The real-time processing of sensing information poses a challenge to the cost of front-end sensing equipment; 2) The real-time transmission of video data puts pressure on the communication network; 3) ) The storage and transmission of video data brings application difficulties to privacy protection issues (such as home care privacy issues)

Images