On-Device Neural Network Validation via Configuration Deviation Monitoring
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Solution Overview
Problem
There is a growing need for on-device validation mechanisms for machine learning models to ensure personalized experiences on mobile devices, as existing solutions rely on dataset-dependent validation methods that are not efficient and raise privacy concerns.
Innovation Solution
A system and method for validating AI models using a validation model that applies anticipated configurational changes, combining outputs from the validation and trained AI models to validate the AI model based on actual deviations, allowing for dataset-independent validation and real-time correction on-device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dataset-dependent validation methods are used, then validation accuracy is improved, but memory requirements and power consumption increase
Solution Approach 1:
The patent extracts the essential validation functionality from dataset-dependent methods by creating a validation model that captures only the necessary configurational relationships. This validation model is a condensed representation that eliminates the need for large validation datasets while retaining the core validation capability, thereby reducing memory and power requirements.
Solution Approach 2:
The patent creates a validation model that serves as a simplified copy or representation of the trained AI model's configurational behavior. This validation model replicates the essential validation function without requiring the full complexity and data requirements of traditional validation approaches, enabling efficient on-device validation.
2Measurement precision
If dataset-dependent validation methods are used, then validation accuracy is improved, but privacy concerns worsen due to user-data transfer
Solution Approach 1:
The patent extracts the validation capability from data-dependent methods and embeds it directly in the validation model. This extraction eliminates the need to transfer user data to external validation systems, keeping all validation operations local to the user's device and thereby protecting privacy while maintaining validation accuracy.
3Reliability
If traditional validation approaches are used, then comprehensive validation is achieved, but device complexity increases
Solution Approach 1:
The patent extracts the essential validation logic from complex traditional validation systems and concentrates it in a streamlined validation model. This extracted validation model performs comprehensive validation checks using a simplified architecture that is suitable for deployment on resource-constrained mobile devices.
Solution Approach 2:
The validation model acts as an intermediary between the trained AI model and the validation process. It mediates the validation function by capturing configurational relationships in a form that enables comprehensive validation without requiring complex external validation infrastructure.
4Adaptability or versatility
If on-device training is performed for personalization, then user experience is improved, but model validation becomes more difficult
Solution Approach 1:
The patent implements a feedback mechanism where the validation model continuously monitors configurational deviations in the trained AI model after personalization training. By comparing actual configurational changes against the validation model's expected variations, the system can detect and measure validation metrics even as the model adapts to user-specific patterns.
Solution Approach 2:
The patent addresses validation difficulty by focusing on configurational parameters rather than raw model weights. The validation model captures relationships between configurational parameters, enabling validation that is invariant to the specific personalization transformations the model undergoes during on-device training.
Data Source
AI summary
A method for validating a trained artificial intelligence (AI) model on a device is provided. The method includes deploying a validation model generated by applying a plurality of anticipated configurational changes associated with the trained AI model requiring validation. Further, the method includes providing input data to each of the validation model and the trained AI model for receiving an output from each of the validation model and the trained AI model, wherein the output of the validation model is further based on one or more actual configurational deviations that occurred during training of the trained AI model since deployment of the trained AI model on the device. Furthermore, the method includes combining the output of each of the validation model and the trained AI model to validate the trained AI model.