ALPRI-FI: A Framework for Early Assessment of Hardware Fault Resiliency of DNN Accelerators

Abstract

Understanding how faulty hardware affects machine learning models is important to both safety-critical systems and the cloud infrastructure. Since most machine learning models, like Deep Neural Networks (DNNs), are highly computationally intensive, specialized hardware accelerators are developed to improve performance and energy efficiency. Evaluating the fault resilience of these DNN accelerators during early design and implementation stages provides timely feedback, making it less costly to revise designs and address potential reliability concerns. To this end, we introduce Architecture-Level Pre-Register-Transfer-Level Implementation Fault Injection (ALPRI-FI), which is a comprehensive framework for assessing the fault resilience of DNN models deployed on hardware accelerators.