Neural Network Resource Adaptation for Efficient Classification
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Solution Overview
Problem
Neural network processing devices face challenges in efficiently managing computing resources, particularly in deployment environments where not all classification features are relevant, leading to unnecessary resource consumption and potential security risks from remote software updates.
Innovation Solution
A method is implemented to execute elements of a computer architecture for classifying physical features, assess the impact of each element, and selectively deallocate computing resources from those with insignificant impact, thereby conserving resources without compromising performance or security, by configuring and de-configuring neural networks based on the specific deployment environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is configured with comprehensive classification features for multiple deployment environments, then the network can handle diverse classification tasks, but computing resources (memory, processor, power) are unnecessarily consumed in environments where not all features are relevant
Solution Approach 1:
The patent implements dynamic configuration of neural network elements by assessing the impact of each element in the deployment environment and selectively deallocating resources from elements with insignificant impact. This allows the system to adapt its computing resources dynamically based on actual environmental needs rather than maintaining a static comprehensive configuration.
Solution Approach 2:
The system changes operational parameters by adjusting which neural network elements are active based on impact assessment. By modifying the configuration state of computing resources (allocated vs. deallocated) based on measured impact, the system optimizes resource usage while maintaining necessary classification capabilities.
2Adaptability or versatility
If comprehensive neural network elements are maintained for all possible deployment scenarios, then the system is prepared for any environment, but security risks increase from unnecessary remote software updates and attack surfaces
Solution Approach 1:
The patent extracts and removes unnecessary neural network elements from the active configuration by assessing impact and deallocating resources from elements with insignificant impact in the specific deployment environment. This extraction reduces the attack surface and eliminates security vulnerabilities associated with unnecessary software components.
Solution Approach 2:
The system discards unnecessary computing resources from the active configuration and recovers them for other uses or simply removes them from operation. This process eliminates security risks from unnecessary elements while preserving the ability to restore or reconfigure if needed.
3Productivity
If all neural network elements are kept active, then performance is maximized for unknown future tasks, but resource efficiency decreases with unnecessary memory, processor, and power usage
Solution Approach 1:
The system performs self-assessment of element impact and self-adjustment of resource allocation without external intervention. By automatically assessing which elements have significant impact and which do not, the system serves itself to optimize resource efficiency while maintaining necessary performance.
Solution Approach 2:
The patent implements a feedback mechanism where the system assesses the impact of each neural network element and uses this information to make informed decisions about resource allocation. This feedback loop ensures that resources are continuously optimized based on actual performance contribution.
Data Source
AI summary
Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to adapt a computing device to classify physical features in a deployment environment. In a particular implementation, computing resources may be selectively de-allocated from at least one of one or more elements of a computing architecture based, at least in part, on assessed impacts to the one or more elements of the computing architecture.