Adaptive Neural Network Layer Reconfiguration for Driving Environments
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Solution Overview
Problem
Existing neural network learning methods require a large amount of data and inefficient memory usage when adapting to various driving environments, leading to suboptimal performance in adaptive AI systems.
Innovation Solution
A method and device for dynamically reconstructing a neural network by changing multiple layers based on determined environmental information, using sensing data from sensors and a processor to adapt the neural network architecture in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple neural networks are stored and learned for various situations, then adaptability to different driving environments is improved, but memory usage efficiency deteriorates
Solution Approach 1:
The neural network device dynamically changes its architecture by selectively activating or deactivating layers based on environmental conditions detected by sensors. This allows the system to adapt to different driving environments without storing multiple complete neural networks, as the same base network can be reconfigured on-demand.
Solution Approach 2:
The neural network is divided into multiple independent layers that can be selectively activated. By segmenting the network into modular layers, the system can activate only the necessary layers for the current environmental condition, reducing memory usage while maintaining adaptability across various driving scenarios.
2Measurement precision
If a large amount of learning data is used to train neural networks for various situations, then recognition rate and understanding accuracy are improved, but learning time and computational resources deteriorate
Solution Approach 1:
The system pre-trains a base neural network on general driving data, and then quickly adapts it to specific environmental conditions by selectively activating pre-configured layers. This preliminary training approach reduces the need for extensive retraining when facing new situations, thereby reducing learning time while maintaining recognition accuracy.
3Device complexity
If the neural network architecture is fixed, then device complexity is reduced, but adaptability to changing environments deteriorates
Solution Approach 1:
The neural network architecture transitions from a fixed structure to a dynamic one where layers can be selectively activated or deactivated based on environmental conditions. This dynamic reconfiguration enables the system to adapt to changing environments without requiring complete architectural redesign, balancing complexity and adaptability.
Data Source
AI summary
A method of operating a neural network device including a plurality of layers, includes receiving sensing data from at least one sensor, determining environmental information, based on the received sensing data, determining multiple layers corresponding to the determined environmental information, and dynamically reconstructing the neural network device by changing at least two layers, among the plurality of layers, to the determined multiple layers.