Analysis and Mitigation of Soft-errors in GPU-accelerated Hyperspectral Image Classifiers

This work assesses the reliability of a hyperspectral image classifier for edge devices under transient faults by using a fine-grain strategy based on the Hardware Injection Through Program Transformation (HITPT) technique. The results identified the most vulnerable software parts and the corruption effects due to hardware faults (from 5.1% to 100.0% of accuracy drop). Then, the results supported the adoption of a selective-hardening software mechanism (based on the Duplication with Comparison strategy) to effectively mitigate the most critical effects under limited costs.