Guardian Classifier Module for Safety-Critical Prediction
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Solution Overview
Problem
Machine learning models in safety-critical systems, such as autonomous vehicles, are prone to prediction errors and hardware-related faults, which can lead to fatal events due to the inherent unpredictability and vulnerability of ML models in hardware implementations.
Innovation Solution
A safety-based prediction system that includes a main classifier module and a guardian classifier module associated with each safety-critical class, where the guardian classifier is trained to predict a binary class, including the safety-critical class and a residual class, to enhance prediction accuracy and error tolerance by using a simpler ML model like Naïve Bayes, and a final predicted class decision module determines the final prediction based on inputs from both classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a machine learning model is implemented in hardware for safety-critical classification tasks, then the system can perform critical classification tasks in autonomous vehicles and robotic medical equipment, but the system is prone to prediction errors and hardware faults that may lead to fatal events
Solution Approach 1:
The system divides the classification task into multiple independent classifiers (main classifier and guardian classifiers) that process the same input data separately. Each classifier produces an independent prediction, and these predictions are then combined through a voting mechanism to determine the final output. This segmentation allows the system to maintain high automation while improving reliability through redundancy and diversity in classification approaches.
Solution Approach 2:
The patent applies different classifier types with different characteristics to the same classification problem. The main classifier and guardian classifiers use different algorithms, training data, or architectural approaches, giving each classifier local quality tailored to its specific role. This diversity in classifier quality helps mitigate the weaknesses of any single classifier and improves overall system reliability.
2Reliability
If additional guardian classifier modules are added to improve prediction accuracy and error detection, then the resilience and accuracy of safety-critical class predictions improve, but the device complexity increases
Solution Approach 1:
The system segments the classification function into modular main classifier and guardian classifier components. Each classifier is an independent module that can be developed, trained, and maintained separately. This modular segmentation manages complexity by organizing the system into discrete, manageable units with clear interfaces, while still achieving improved reliability through the combined operation of multiple classifiers.
Solution Approach 2:
The guardian classifiers serve multiple functions: they act as independent prediction engines, provide error detection through comparison with main classifier outputs, and contribute to the final decision through voting. This multi-functionality justifies the added complexity by demonstrating that the additional components provide multiple benefits beyond simple redundancy.
3Reliability
If multiple classifiers are used to enhance safety and accuracy, then the system can detect potential hardware-related errors and improve resilience, but the cost of implementing additional classifiers increases
Solution Approach 1:
The system creates simplified copies of the classification function through guardian classifiers that use different algorithms or training data. These copies are less computationally intensive than full-featured classifiers but provide sufficient predictive capability for safety verification. This copying approach enables error detection and improved reliability while controlling implementation costs through the use of lighter-weight classifier models.
Data Source
AI summary
A safety-based prediction apparatus, system and method are provided. A machine learning hardware accelerator (MLHA) includes a main classifier (MC) module, at least one guardian classifier (GC) module, and a final predicted class decision module. The MC module predicts an MC predicted class based on input data, and includes a pre-trained, machine learning main classifier (MLMC) that has at least one safety critical (SC) class and a plurality of non-SC classes. Each guardian classifier (GC) module is associated with an SC class, and predicts a GC predicted class based on the input data. Each GC module includes a pre-trained, machine learning guardian classifier (MLGC) having two classes including an associated SC class and a residual class that includes any non-associated SC classes and the plurality of non-SC classes. A decision module determines and outputs a final predicted class based on the MC predicted class and each GC predicted class.