Spatially Sparse Convolution Module for Visual Rendering
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Solution Overview
Problem
Current graphics processing systems face inefficiencies in parallel data processing, particularly in visual rendering and synthesis, due to limitations in handling large datasets and complex operations within the graphics pipeline.
Innovation Solution
The implementation of a spatially sparse convolution module within a graphics processing unit (GPU) that accelerates graphics operations and machine-learning tasks by optimizing data processing through parallel processing techniques, such as SIMT architectures and dedicated circuitry for efficient command execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional fixed function computational units are used for graphics data processing, then processing reliability is maintained, but processing speed and adaptability deteriorate
Solution Approach 1:
The patent transforms fixed function computational units into dynamic, reconfigurable processing elements. The graphics processor uses programmable shader units that can be dynamically configured to perform different operations (vertex shading, fragment shading, geometry processing) based on the specific rendering task, enabling both high speed and adaptability without requiring separate dedicated hardware for each function.
Solution Approach 2:
The patent implements universal processing units that can perform multiple graphics processing functions. The shader units and processing clusters are designed to handle various operations including transformation, lighting, texture mapping, and post-processing effects using the same hardware resources, eliminating the need for separate dedicated units for each function and improving overall processing efficiency.
2Productivity
If parallel processing techniques are implemented to increase throughput, then productivity improves, but device complexity and computational overhead increase
Solution Approach 1:
The patent divides the graphics processing workload into segmented processing stages handled by specialized processing clusters. The architecture segments functions into vertex processing, fragment processing, and geometry processing clusters, each optimized for specific tasks. This segmentation enables parallel execution of multiple operations simultaneously while maintaining manageable complexity through functional specialization.
Solution Approach 2:
The patent utilizes multi-dimensional parallelism by organizing processing elements in hierarchical clusters that operate simultaneously across multiple dimensions - within-cluster parallelism for individual operations, between-cluster parallelism for different processing stages, and temporal parallelism through pipeline stages. This multi-dimensional approach maximizes throughput while distributing complexity across multiple levels of the architecture.
3Productivity
If SIMT architecture is used to maximize parallel thread execution, then processing efficiency improves, but difficulty of detecting and measuring performance increases
Solution Approach 1:
The patent implements performance monitoring and feedback mechanisms that track thread execution metrics, occupancy rates, and resource utilization across the SIMT architecture. The graphics processor includes performance counters and profiling capabilities that provide real-time feedback on parallel thread execution efficiency, enabling developers to optimize shader code and understand performance characteristics without requiring complex external measurement tools.
Data Source
AI summary
Embodiments are generally directed to methods and apparatuses of spatially sparse convolution module for visual rendering and synthesis. An embodiment of a method for image processing, comprising: receiving an input image by a convolution layer of a neural network to generate a plurality of feature maps; performing spatially sparse convolution on the plurality of feature maps to generate spatially sparse feature maps; and upsampling the spatially sparse feature maps to generate an output image.