Parallel Processing System for Real-Time Computer Vision
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Solution Overview
Problem
Conventional processing systems are ineffective in performing computationally intensive computer vision tasks such as classification, object detection, and 3D graphics and modeling in real-time, hindering the performance of computer vision applications.
Innovation Solution
A highly parallel processing system comprising a central processing unit and a highly parallel processing unit with hundreds to thousands of parallel processing cores, utilizing libraries of graphics codes written in low-level programming languages to execute tasks in parallel, enabling the CPU to offload heavy computational tasks to the GPU or similar units for accelerated processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional processing systems are used to perform computationally intensive computer vision tasks, then the system structure is simple and easy to operate, but the processing speed is insufficient and real-time performance cannot be achieved
Solution Approach 1:
The system divides computational tasks into two segments: the CPU handles application logic and coordination, while the GPU handles computationally intensive parallel processing tasks. This segmentation allows each processor to specialize in its strengths, achieving real-time performance for computer vision tasks without requiring the entire system to be redesigned for high-speed parallel processing.
Solution Approach 2:
The GPU is utilized for multiple purposes: traditional graphics rendering and computationally intensive computer vision tasks such as image classification, object detection, and 3D modeling. This multi-functionality allows the system to achieve high processing speeds for diverse computational tasks without adding separate specialized hardware for each function.
2Productivity
If conventional processing systems are used for computer vision tasks, then the device complexity is low, but the productivity for heavy computational tasks is insufficient
Solution Approach 1:
The system segments computational workload between CPU and GPU, with the GPU dedicated to handling computationally intensive tasks in parallel. This segmentation enables high productivity for computer vision algorithms that require processing large numbers of pixels and data points simultaneously, while the CPU manages overall system coordination.
Solution Approach 2:
The system replaces traditional sequential processing mechanisms with parallel processing mechanisms by utilizing the GPU's array of processing cores. This substitution enables simultaneous execution of multiple computational operations, dramatically increasing productivity for tasks such as image classification and object detection that require processing vast amounts of data.
Data Source
AI summary
Disclosed is a highly parallel processing system for processing graphics applications written in a high-level programming language. The high-performance computing architecture includes a graphics processing unit with numerous processing cores, such as hundreds to thousands of processing cores. The graphics processing unit includes routines written in a low-level programming language. The routines of the graphics processing unit are invoked to process highly computational intensive tasks by the numerous processing cores in parallel.