Frozen and Fluid Neural Network Nodes for ROI Processing
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Solution Overview
Problem
Neural networks require significant computational resources to process large amounts of image data, particularly when analyzing multiple images with regions of interest, leading to inefficiencies in energy consumption and processing time.
Innovation Solution
The neural network architecture includes fluid and frozen nodes, where nodes linked to regions of interest are updated, and others remain in a paused state, conserving resources by only processing nodes directly linked to relevant image pixels, and preserving results for frozen nodes to avoid recalculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all nodes in the neural network are updated during processing, then processing accuracy is maintained, but computational resources and energy consumption increase significantly
Solution Approach 1:
The neural network nodes are segmented into two distinct groups: fluid nodes that are updated during processing and frozen nodes that retain previous values. This segmentation allows the system to update only the necessary subset of nodes (those connected to changed image pixels) while keeping other nodes frozen, thereby reducing computational load and energy consumption while maintaining processing accuracy for relevant features.
2Reliability
If all nodes are reprocessed for each image, then processing completeness is ensured, but processing time increases
Solution Approach 1:
Nodes are prepared in advance with preliminary values from previous image processing. Frozen nodes retain these pre-computed values, eliminating the need to recalculate them for every new image. This preliminary action ensures that when a new image is processed, only the necessary fluid nodes are updated, maintaining processing completeness while significantly reducing processing time.
3Loss of energy
If the neural network processes only relevant regions, then energy consumption decreases, but computational complexity increases due to node state management
Solution Approach 1:
Different quality states (fluid and frozen) are assigned to different nodes based on their local relevance to the current processing task. Nodes connected to changed image pixels are designated as fluid with full update capability, while other nodes remain frozen. This local differentiation reduces energy consumption by focusing computation only where needed, and the complexity of state management is minimized through clear criteria for state assignment.
Data Source
AI summary
A first image can be processed through a neural network. The first image can cause each first hidden node to assume a first state. A second image can be accessed along with a region-of-interest (ROI) for the second image. At least some of the first hidden nodes can be frozen based on the ROI. At least some of the first hidden nodes can be kept fluid based on the ROI. The second image can be processed through the neural network. The fluid first hidden nodes can assume second states. The frozen first hidden nodes can remain in the first state.