Dynamic Binning via Variable-Sized Segmentation for GPU Efficiency
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Solution Overview
Problem
Current graphics processing unit (GPU) binning methods are inefficient in dynamically adjusting bin sizes based on image frame characteristics, leading to suboptimal processing times and resource utilization.
Innovation Solution
A method and system that segment pre-image frames into variable-sized bins, with different dimensions and centers, using weighted Chebyshev distance for bin placement, allowing for dynamic binning and parallel processing to improve GPU efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform bin sizes are used for all regions of the pre-image frame, then the binning structure is simple and easy to implement, but processing efficiency is suboptimal due to inability to adapt to varying image characteristics
Solution Approach 1:
The pre-image frame is segmented into multiple bins of varying sizes rather than uniform sizes. Each bin's dimensions are dynamically determined based on the density of image features in that region, allowing the system to process complex regions with more detail while using coarser processing for simpler regions, thereby improving overall processing efficiency without requiring complete structural redesign
Solution Approach 2:
Different regions of the pre-image frame are assigned different bin sizes according to their local characteristics. Regions with high feature density receive smaller bins for detailed processing, while regions with low feature density receive larger bins for efficient coarse processing. This local adaptation resolves the contradiction by making the binning structure complex only where necessary
2Productivity
If variable-sized bins are used to optimize processing, then processing efficiency improves, but the complexity of determining bin dimensions and placement increases
Solution Approach 1:
The system performs preliminary analysis of the pre-image frame to identify feature densities and determine optimal bin configurations before actual image processing begins. This advance preparation of bin structures based on pre-analyzed image characteristics reduces the complexity burden during the main processing phase, as the bin determination is completed in advance rather than dynamically during processing
Solution Approach 2:
The binning structure is made dynamic and adaptive rather than static. Bin dimensions and placements are adjusted based on the actual content and feature distribution of each pre-image frame, allowing the system to optimize processing efficiency for each specific input while managing complexity through adaptive algorithms that respond to actual image characteristics
3Productivity
If larger bins are used to reduce the number of processing units, then resource utilization improves, but processing precision decreases for detailed regions
Solution Approach 1:
The binning strategy applies different bin sizes to different spatial regions based on their processing requirements. High-detail regions receive smaller bins that preserve processing precision, while low-detail regions receive larger bins that improve resource utilization. This local differentiation resolves the contradiction by applying precision only where necessary and efficiency where appropriate
Solution Approach 2:
The image frame is segmented into multiple bins with strategically varied sizes. This segmentation allows the system to allocate processing resources more effectively by grouping pixels into appropriately-sized bins for their respective regions, thereby improving overall resource utilization while maintaining sufficient precision in critical areas through the segmented structure
Data Source
AI summary
A system and method for binning. In some embodiments, the method includes segmenting a first pre-image frame into a first plurality of bins; and processing each of the bins to form a respective pixel subarray, the first plurality of bins including: a first bin having first dimensions, and a second bin having second dimensions, different from the first dimensions.