Neural Network Algorithm Replacement for Memory Reduction
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Solution Overview
Problem
Artificial neural networks face challenges in resource usage, particularly memory and processing power, as they become more complex and are applied to diverse tasks, leading to increased demands that can be difficult to manage with existing computer resources.
Innovation Solution
A method and system for reducing resource usage in artificial neural networks by capturing and replacing algorithms during execution, identifying candidate algorithms that use less memory, and updating the network with these more efficient algorithms, allowing for optimized performance without prior knowledge of the network layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the artificial neural network uses more complex algorithms to improve performance, then the accuracy and speed of image processing are improved, but the memory usage and processing power requirements increase
Solution Approach 1:
The patent applies dynamics by making the algorithm selection adaptive and iterative. The system dynamically switches between different algorithms (e.g., Sobel, Canny, Hough transforms) based on real-time performance monitoring and memory usage conditions. This allows the neural network to optimize its computational approach during execution, achieving high processing speed while managing memory consumption through flexible algorithm selection rather than fixed complex operations
Solution Approach 2:
The patent changes parameters by monitoring performance metrics and memory usage in real-time, then adjusting algorithm parameters accordingly. When memory usage exceeds thresholds or performance degradation is detected, the system modifies operational parameters by selecting alternative algorithms with different computational characteristics. This parameter adaptation enables the system to maintain optimal balance between processing speed and memory efficiency without requiring static complex algorithms
2Measurement precision
If the artificial neural network executes more iterations to improve accuracy, then the detection precision is improved, but the time consumption and resource usage increase
Solution Approach 1:
The patent implements feedback by continuously monitoring the performance of each algorithm iteration and using this information to guide future selections. The system tracks metrics such as detection accuracy, processing time, and memory usage, then feeds this information back into the algorithm selection mechanism. This feedback loop enables the system to achieve high detection precision through selective iteration while minimizing total processing time by avoiding redundant or inefficient computational cycles
Solution Approach 2:
The patent applies partial action by executing only the necessary number of iterations required to achieve acceptable detection accuracy. Rather than performing excessive iterations that would waste resources, the system monitors performance and stops or switches iterations when marginal gains no longer justify the computational cost. This partial execution strategy maintains sufficient measurement precision while significantly reducing time consumption and resource usage
3Power
If the existing computer resources are used to handle complex neural network tasks, then the processing capability is sufficient, but the resource management becomes difficult and inefficient
Solution Approach 1:
The patent applies self-service by enabling the neural network to automatically manage its own resource consumption. The system includes built-in monitoring mechanisms that track memory usage, processing time, and computational efficiency, then automatically adjust algorithm selection and execution parameters without external intervention. This self-managed approach maintains sufficient processing capability for complex tasks while dramatically reducing resource management complexity through autonomous optimization rather than requiring complex external resource coordination
Data Source
AI summary
A generated algorithm used by a neural network is captured during execution of an iteration of the neural network. A candidate algorithm is identified based on the generated algorithm. A determination is made that the candidate algorithm utilizes less memory than the generated algorithm. Based on the determination the neural network is updated by replacing the generated algorithm with the candidate algorithm.