Runtime Model Injection for Selective ML Controller Updates
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Solution Overview
Problem
The retraining of machine learning models is complex and time-consuming, leading to suboptimal and unreliable control of technical units, such as industrial robots and autonomous vehicles, due to domain drifts and the need for switching between high-performance and high-assurance controllers, resulting in safety hazards and operational inefficiencies.
Innovation Solution
A technical injection system comprising two computing units, where one unit preprocesses and stores a retrained machine learning model independently and injects the relevant parts into the second unit at runtime, allowing seamless and reliable updates without interrupting the control process, using an injection interface to replace the core of the current model with the retrained model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the machine learning model is retrained to adapt to domain drift, then the model performance and reliability are improved, but the retraining process is complex and time-consuming
Solution Approach 1:
The patent segments the machine learning model into multiple independent components or layers, allowing selective retraining and injection of specific parts rather than retraining the entire model. This reduces the time and computational resources required while maintaining model performance.
Solution Approach 2:
The patent performs preliminary retraining of model components in advance, prepares the retrained parts, and stores them for later injection. This allows the retraining process to be completed beforehand, reducing the time required during actual deployment when domain drift is detected.
2Stability of the object's composition
If the high-assurance controller is used longer to allow model retraining, then system stability is maintained, but the control reliability decreases due to suboptimal outputs
Solution Approach 1:
The patent extracts only the necessary parts of the retrained model that need to be updated, rather than replacing the entire model. This allows for faster injection and reduces the time the system must rely on suboptimal controllers, thereby maintaining both stability and reliability.
Solution Approach 2:
The patent introduces an intermediary injection mechanism that facilitates the transfer and integration of retrained model parts into the running system. This intermediary process enables seamless updates without requiring prolonged use of backup controllers, thus maintaining control reliability while ensuring stability.
3Reliability
If control switches between high-performance and high-assurance controllers, then model reliability is maintained, but energy consumption increases and operational stability decreases
Solution Approach 1:
The patent implements a dynamic model injection system that adapts to changing conditions by injecting retrained model parts only when necessary. This dynamic approach avoids unnecessary switching between controllers, reducing energy consumption while maintaining model reliability through targeted updates.
4Adaptability or versatility
If the entire machine learning model is replaced during retraining, then the model adapts to new data distributions, but the complexity and time required for retraining increases significantly
Solution Approach 1:
The patent divides the machine learning model into separable components that can be independently retrained and injected. This segmentation reduces retraining complexity by allowing focused updates on specific parts rather than the entire model, while still achieving adaptability to new data distributions.
Solution Approach 2:
The patent performs preliminary retraining of model components and prepares them for injection in advance. This preliminary action reduces the complexity and time of the actual model update process, while ensuring the model adapts to new data distributions through pre-prepared retrained parts.
Data Source
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AI summary
The invention is directed to a technical injection system (1) for injecting a retrained machine learning model (30), comprising a. a first computing unit (10) comprising a first storage medium (12), wherein the first computing unit (10) is configured for providing the retrained machine learning model (30); and preprocessing the retrained machine learning model (30); wherein the retrained machine learning model (30) is stored in the first storage medium (12); b. a second computing unit (20) comprising a second storage medium (22) and an injection interface (40), wherein the injection interface (40) is configured for injecting at least one relevant part of the retrained machine learning model (30) after processing from the first storage medium (12) of the first computing unit (10) into the second storage medium (22) of the second computing unit (20) by means of the injection interface at runtime; wherein a current machine learning model is stored in the second storage medium (22); and the injection comprises the identification of the at least one relevant part of the current machine learning model and the replacement of the identified at least one relevant part of the current machine learning model by the corresponding at least one relevant part of the retrained machine learning model.