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Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems
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DOI：10.1109/TR.2024.3355408.png)
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Abstract
En 中文
The complexity of machine learning (ML) systems increases each year. As these systems are widely utilized, ensuring their reliable operation is becoming a design requirement. Traditional error detection mechanisms introduce circuit or time redundancy that significantly impacts system performance. An alternative is the use of concurrent error detection (CED) schemes that operate in parallel with the system and exploit their properties to detect errors. CED is attractive for large ML systems because it can potentially reduce the cost of error detection. In this article, we introduce concurrent classifier error detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors. CCED identifies a set of check signals in the main ML system and feed them to the concurrent ML classifier that is trained to detect errors. The proposed CCED scheme has been implemented and evaluated on two widely used large-scale ML models: Contrastive language-image pretraining (CLIP) used for image classification and bidirectional encoder representations from transformers (BERT) used for natural language applications. The results show that more than 95% of the errors are detected when using a simple Random Forest classifier that is orders of magnitude simpler than CLIP or BERT.
Keywords:
Bidirectional encoder representations from transformers (BERT)
concurrent error detection (CED)
contrastive language-image pretraining (CLIP)
machine learning (ML)
soft errors
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