Neural Network Training Cost Function Resource Constraints
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Solution Overview
Problem
Conventional methods for training artificial neural networks often result in overly complex models that exceed the resource limitations of devices like mobile devices or embedded systems, limiting their performance due to the need for extensive training data and computing operations.
Innovation Solution
A method that incorporates a cost function accounting for available resources, employing pruning and quantization techniques to reduce neural network complexity while maintaining optimal performance, by distributing resources across layers and adjusting bit sizes and filter numbers based on hardware constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network complexity is increased to improve performance and safety, then the model accuracy and safety are improved, but the resource consumption exceeds the capacity of mobile devices or embedded systems
Solution Approach 1:
The patent applies parameter changes by modifying the cost function to include complexity regularization terms that penalize excessive model complexity. This allows the neural network to be trained with adjusted parameters (weight decay, dropout rates, architecture constraints) that balance performance and resource usage, enabling deployment on resource-constrained devices while maintaining safety requirements
Solution Approach 2:
The patent extracts and separates the complexity management aspect from the standard training process by introducing a dedicated complexity regularization component in the cost function. This extracted complexity control mechanism operates independently to prune or constrain the network architecture during training, removing unnecessary complexity while preserving essential functionality for safety-critical tasks
2Measurement precision
If more training data and computing operations are used to improve model performance, then the learning accuracy is improved, but the resource consumption exceeds the capacity of conventional data processing systems
Solution Approach 1:
The patent applies partial action by implementing selective training strategies where not all possible training data or computing operations are executed. The complexity-regularized cost function enables partial utilization of training resources by focusing computation on the most informative data samples and operations, achieving satisfactory learning accuracy without exhaustively processing all available training data
Solution Approach 2:
The patent modifies training parameters by incorporating complexity regularization terms in the cost function that control the trade-off between learning accuracy and resource consumption. By adjusting parameters such as regularization strength, batch size, and learning rate schedules, the system achieves optimal learning performance within constrained computational resources of mobile or embedded devices
3Measurement precision
If the neural network architecture is optimized for performance, then the model accuracy is improved, but the model complexity exceeds the resource limitations of target devices
Solution Approach 1:
The patent applies parameter changes by modifying the cost function to include complexity regularization terms that penalize excessive model complexity. This allows the neural network to be trained with adjusted parameters (weight decay, dropout rates, architecture constraints) that balance performance and resource usage, enabling deployment on resource-constrained devices while maintaining safety requirements
Solution Approach 2:
The patent implements feedback mechanisms where the complexity regularization term continuously monitors and adjusts the model complexity during training. The cost function provides feedback signals that guide the optimization process to maintain model accuracy while preventing complexity from exceeding device capabilities, creating a self-regulating training system
Data Source
AI summary
A method for training an artificial neural network. The method includes the following steps: providing training data for training the artificial neural network; detecting at least one specification regarding available resources; ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources; and training the artificial neural network on the basis of the provided training data using the ascertained cost function.