Multi-Channel Image Processing for Faster Weight Gradient Calculation
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Solution Overview
Problem
The technical efficiency of reverse weight gradient calculation in traditional deep learning technologies is low, failing to meet user needs due to insufficient computing power and lack of specific implementation details in existing solutions.
Innovation Solution
A method utilizing dedicated processing units to perform parallel pixel extractions and splicing of pixel point data for each channel of an input image, optimizing the format processing of input images of various sizes to enhance the throughput of weight gradient calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sequential processing methods are used for reverse weight gradient calculation, then implementation simplicity is maintained, but calculation throughput is insufficient
Solution Approach 1:
The patent divides the input image into multiple channels and processes each channel independently using separate processing units. This segmentation enables parallel computation of pixel extractions across different channels, significantly improving calculation throughput for reverse weight gradient operations without requiring a complete redesign of the processing architecture.
Solution Approach 2:
The patent introduces channel dimension as an additional processing dimension, transforming the traditional single-stream processing into multi-channel parallel processing. By organizing data and operations along the channel dimension, the system achieves higher throughput while maintaining a structured and manageable device architecture.
2Productivity
If parallel processing with multiple dedicated units is implemented, then calculation efficiency is improved, but data organization complexity increases
Solution Approach 1:
The patent performs pixel extractions in parallel across multiple channels before combining the results. By preparing and organizing data from each channel independently in advance, the system simplifies the subsequent combination process and reduces the overall data organization complexity despite using multiple processing units.
Solution Approach 2:
The patent combines the results from multiple channel processing units into a unified output structure. This merging operation consolidates the parallel processing results in a systematic way, managing the complexity introduced by multiple processing units while maintaining high processing efficiency.
3Ease of operation
If im2col operation with matrix multiplication is used, then convolution operation is simplified, but reverse weight gradient calculation becomes computationally intensive
Solution Approach 1:
The patent applies the im2col operation and matrix multiplication approach in reverse for the weight gradient calculation. Instead of following the traditional forward calculation path, the system performs pixel extractions and matrix operations in the reverse direction, enabling efficient computation of weight gradients while maintaining the mathematical simplicity of matrix operations.
Data Source
AI summary
A method for processing an image, an electronic device, and a storage medium are provided. The method includes: acquiring an input image, the input image including a plurality of channels, and each channel of the plurality of channels including a plurality of pixel points; performing pixel extractions in parallel using a plurality of dedicated processing units on each channel of the input image, to obtain pixel point data for each corresponding pixel point of each channel; and splicing the pixel point data for each corresponding pixel point of each channel to obtain an output image.