Coordinate-Selected Kernel Grids for Mobile Neural Image Processing
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Solution Overview
Problem
Conventional neural network approaches for real-time image processing in mobile devices are hindered by high computational overhead and memory demands due to generating unique kernels for each pixel, limiting their practical deployment in power and memory-constrained environments.
Innovation Solution
A learnable kernel classification system where a neural network is trained to select and apply pre-learned kernels from a stored grid, reducing computational and memory requirements by predicting coordinates for kernels rather than generating them on the fly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If kernel-predicting convolutional networks dynamically create filters for each pixel, then image processing capabilities are improved, but computational overhead and memory bandwidth requirements increase significantly
Solution Approach 1:
The patent pre-learns and stores a finite set of kernels in memory during training, so that during inference the network only needs to select and retrieve appropriate pre-computed kernels rather than generating new kernels for each pixel. This preliminary preparation of kernels resolves the contradiction by maintaining adaptive processing capabilities while dramatically reducing real-time computational overhead and memory bandwidth requirements.
Solution Approach 2:
Instead of generating unique kernels for each pixel, the network outputs coordinates that reference copies of pre-stored kernels from a finite set. Multiple pixels can share the same kernel copy, reducing the overall computational burden while maintaining the ability to apply appropriate kernels to different image regions. This copying approach preserves image processing versatility without proportionally increasing device complexity.
2Measurement precision
If unique kernels are generated for each pixel, then processing accuracy is improved, but memory bandwidth requirements increase
Solution Approach 1:
Kernels are pre-computed and stored in memory during the training phase, eliminating the need to generate and store new kernel data for each pixel during inference. The network only needs to retrieve pre-existing kernels using coordinate outputs, which dramatically reduces memory bandwidth consumption while maintaining processing accuracy through the use of pre-learned optimal kernels.
Solution Approach 2:
The patent extracts the computationally intensive kernel generation process from the real-time inference pipeline and separates it into a preliminary training phase. During inference, only lightweight coordinate prediction and kernel retrieval operations remain, extracting the heavy computational burden from the forward pass and resolving the contradiction between accuracy and memory bandwidth requirements.
3Adaptability or versatility
If neural networks are trained to generate kernels dynamically, then transformation capabilities are improved, but device power consumption increases
Solution Approach 1:
The energy-intensive kernel generation process is performed once during training when the device is stationary or has access to more power. During mobile inference, the network only performs lightweight coordinate prediction and kernel selection, dramatically reducing real-time power consumption while preserving full transformation capabilities through the pre-learned kernel set.
Solution Approach 2:
Instead of repeatedly performing expensive kernel generation operations for each pixel, the system uses simple coordinate copying and lookup operations during inference. This replacement of complex computations with simple retrieval operations maintains transformation versatility while reducing device power consumption to levels suitable for mobile platforms.
4Productivity
If pre-learned kernels are selected from a grid, then computational resources are reduced, but network capacity must be optimized
Solution Approach 1:
The network is trained to output coordinates that directly index into a pre-organized grid of kernels. This preliminary structuring of kernels in a searchable grid format, combined with training the network to predict coordinates rather than full kernel values, dramatically improves computational efficiency during inference while keeping the network architecture relatively simple and manageable in capacity.
Data Source
AI summary
A system and a method are disclosed for generating an output image using a learnable kernel classification network. The method including applying a neural network to an input image to output one or more coordinates of kernels stored in a grid; identifying one or more kernels stored in the grid of kernels corresponding to the one or more coordinates; and applying the one or more kernels to one or more regions of the input image to generate the output image.