Adaptive Deformable Kernel Prediction Network for GPU Image De-noising
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Solution Overview
Problem
Current graphics processing units (GPUs) face limitations in efficiently processing graphics data and machine learning operations due to fixed function computational units and the need for more adaptive processing techniques, particularly in parallel graphics data processing.
Innovation Solution
The implementation of an Adaptive Deformable Kernel Prediction Network (ADKPN) that enhances image de-noising capabilities by utilizing a GPU with adaptive kernel prediction, allowing for more efficient processing of graphics and machine learning tasks through dynamic kernel adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed function computational units are used in GPUs, then device complexity is reduced and ease of manufacture is improved, but adaptability to different processing tasks deteriorates
Solution Approach 1:
The patent implements dynamic computational units that can adapt their function based on input data characteristics. The system dynamically switches between different processing modes (e.g., de-noising, super-resolution, style transfer) without requiring physical reconfiguration, thereby achieving versatility while maintaining manageable complexity through software-controlled adaptability.
Solution Approach 2:
The patent changes operational parameters of computational units to achieve different processing functions. By adjusting kernel parameters, filter sizes, and processing algorithms based on input image characteristics, the system achieves adaptability to various tasks without changing the underlying hardware architecture.
2Adaptability or versatility
If adaptive processing techniques are implemented, then adaptability to varying data conditions is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary analysis of input data characteristics before processing to determine the appropriate processing mode and parameters. This preliminary action enables the system to adapt to varying data conditions by pre-configuring the computational units with suitable parameters, reducing the complexity of real-time adaptation during actual processing.
Solution Approach 2:
The patent implements feedback mechanisms where processing results are evaluated and used to adjust subsequent processing parameters. This feedback loop enables adaptive processing by continuously optimizing computational parameters based on actual data characteristics and processing outcomes, achieving adaptability through iterative refinement rather than complex upfront configuration.
3Productivity
If parallel processing is maximized in SIMT architecture, then productivity is improved, but measurement precision of individual processing operations deteriorates
Solution Approach 1:
The patent segments parallel processing into distinct stages: a analysis stage that processes data characteristics with high precision to determine processing parameters, and an execution stage that applies these parameters in parallel. This segmentation allows precision-critical operations to be performed serially while maintaining high overall productivity through parallel execution of standardized processing kernels.
Data Source
AI summary
Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.