Work-in-Progress: Accuracy-Area Efficient Online Fault Detection for Robust Neural Network Software-Embedded Microcontrollers

Detecting transient faults in safety-critical neural network (NN) applications operated on embedded systems has become a concern, but it is challenging to achieve high accuracy because of the open context problem and resource constraints. This study proposes an accuracy-area efficient, data-analysis-based online soft errors (SEs) and control flow errors (CFEs) detection, applicable to any NN application with low overhead. We insert code for runtime monitoring data assertion, and the data are distributed to shallow or deep detection models selectively. The shallow detection model detects CFEs by verifying runtime signatures with values obtained from simulations, and detects SEs of data having constant values according to program input. SEs of other data are verified by a deep detection model using a sliding window one-class support vector machine. Fault injection experiments on an image classification NN showed that our detector has significant detection accuracy in fault conditions.