GENIE: GENetIc Algorithm-Based REliability Assessment Methodology for Deep Neural Networks

As deep neural networks (DNNs) are becoming vital to numerous safety-critical applications, ensuring their fault reliability is crucial. This paper presents a hybrid framework that combines genetic algorithms with fault injection and analytical methods to identify vulnerable neurons and layers in DNNs. Validated on LeNet-5, AlexNet, and VGG-11, our approach significantly enhances model accuracy in harsh environments by protecting critical components, achieving reliability improvements (accuracy drop of the model compared to the golden network after fault injection) of $69.86 \%$ for LeNet, and 99.65% for AlexNet at BER=1E-4, and 91.37% improvement for VGG at $\mathrm{BER}=1 \mathrm{E}-6$. Furthermore, our framework reduces computational costs by requiring fewer inferences than traditional analytical methods, highlighting its potential to improve DNN accelerators’ reliability and contribute to their safety.