Multi-Dimensional Processor Architecture for Low-Latency Image Recognition
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Solution Overview
Problem
Conventional processing systems face mismatches in processing speed and data transfer speed, leading to reduced overall system performance and inability to handle complex computational processes efficiently, particularly in image processing and graphics processing.
Innovation Solution
A parallelized architecture for multi-dimensional data processing is implemented, utilizing a system-on-chip (SoC) with multiple processors configured to execute frame data in different dimensions in parallel, including a graphics processing unit (GPU) and pixel processing engines (PPEs) to accelerate image feature recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional processing systems are used, then device complexity is reduced, but processing speed and system performance deteriorate due to mismatches between processing components
Solution Approach 1:
The system segments processing tasks across multiple specialized processors (2D processor for spatial operations, 1D processor for temporal operations, N-D processor for complex multi-dimensional data). Each processor handles specific types of frame data operations, eliminating the bottleneck of conventional single-processor systems and achieving parallel processing of video streams.
Solution Approach 2:
The patent introduces multi-dimensional processing capabilities by adding processors that operate in different dimensional spaces (2D spatial, 1D temporal, N-D hybrid). This dimensional diversification allows simultaneous processing of different aspects of frame data, resolving the speed-performance tradeoff through architectural expansion rather than incremental improvement.
2Productivity
If conventional processors are used, then device complexity is minimized, but productivity decreases due to redundant data loading operations
Solution Approach 1:
The system merges multiple processors with complementary capabilities into a unified processing architecture. The 2D processor handles spatial transformations, the 1D processor handles temporal sequences, and the N-D processor coordinates multi-dimensional operations, creating a synergistic system that processes video data more efficiently than the sum of individual processors.
Solution Approach 2:
Each processor in the system is designed with universal capabilities to handle different types of frame data operations. The processors can adaptively process various data formats and operations (spatial filtering, temporal interpolation, multi-dimensional transformations) based on the specific requirements of the video stream, maximizing productivity across diverse processing tasks.
3Measurement precision
If conventional processors are used, then ease of operation is maintained, but measurement precision of image features deteriorates at high processing speeds
Solution Approach 1:
The system applies local quality optimization by assigning specialized processing capabilities to specific processors based on the type of operation required. The 2D processor provides high-precision spatial feature extraction, the 1D processor delivers accurate temporal analysis, and the N-D processor ensures precise multi-dimensional coordinate transformations, maintaining measurement precision across all processing dimensions simultaneously.
Data Source
AI summary
Aspects of this technical solution can increase speed of processing in low-latency application areas, while maintaining integrity of image feature recognition at those higher speeds. For example, in image-processing environments associated with autonomous navigation (e.g., driving), a large volume of image data is to be rapidly and accurately processed to maintain reliable and up-to-date models of a physical environment. For example, embodiments in accordance with this disclosure can provide high-speed and accurate image feature recognition of input frame data beyond the capability of CPU processing or general GPU processing to achieve.