Neural Network Inference for High-Resolution Image Processing
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Solution Overview
Problem
Processing high-resolution inputs, such as images, with certain neural networks, such as transformers, requires significant processing time and memory, leading to memory constraints and processing inefficiencies.
Innovation Solution
A multi-stage inference process is implemented, where a neural network, like a vision transformer, reduces high-resolution inputs to lower resolutions, divides relevant portions for further processing, and eliminates irrelevant portions, using techniques to efficiently utilize processor capacity and bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution inputs are processed directly with neural networks, then processing accuracy is improved, but processing time and memory requirements increase significantly
Solution Approach 1:
The patent divides the high-resolution input processing into multiple stages: first processing a downsampled version to identify regions of interest, then selectively processing only those relevant regions at full resolution. This segmentation approach maintains accuracy for important areas while reducing overall processing time and memory usage.
Solution Approach 2:
The patent applies different processing qualities to different regions of the input. Regions identified as containing objects of interest receive full-resolution processing for high accuracy, while other regions are processed at lower resolution or skipped entirely, optimizing the trade-off between accuracy and processing efficiency.
2Measurement precision
If high-resolution inputs are processed directly with neural networks, then processing accuracy is improved, but memory constraints are worsened
Solution Approach 1:
The patent segments the memory requirements by loading and processing only relevant portions of the high-resolution input at full resolution, while keeping the rest in a downsampled form. This reduces the peak memory requirements compared to loading the entire high-resolution input into memory simultaneously.
Solution Approach 2:
The patent implements a nested processing structure where a downsampled version of the input (containing essential information at reduced detail) is nested within the full-resolution processing of selected regions. This allows the system to maintain access to full-resolution data where needed while using the downsampled version to guide processing and reduce overall memory consumption.
3Productivity
If relevant portions are selected and irrelevant portions are eliminated, then processing efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary processing by first downsampling the input and identifying regions of interest before proceeding to full-resolution processing of selected areas. This preliminary action simplifies the subsequent processing by reducing the amount of data that requires intensive computation, thereby improving overall efficiency despite the added step.
Solution Approach 2:
The patent introduces a downsampled version of the input as an intermediary that guides the selection of regions for full-resolution processing. This intermediary structure facilitates efficient processing by providing a low-cost mechanism to identify important areas without requiring full-resolution analysis of the entire input.
Data Source
AI summary
Apparatuses, systems, and methods to use one or more neural networks to generate information about one or more images based, at least in part, on one or more confidence scores associated with the information. In at least one embodiment, a neural network downscales an image and performs an image processing task on said downscaled image according to a query input by a user.