The Impact of Soft Errors in Memory Units of Edge Devices Executing Convolutional Neural Networks
Geancarlo Abich, Rafael Garibotti, Ricardo Reis, Luciano Ost
Abstract
Driven by the success of machine learning algorithms for recognizing and identifying objects, there are significant efforts to exploit convolutional neural networks (CNNs) in edge devices. The growing adoption of CNNs in safety-critical embedded systems (e.g., autonomous vehicles) increases the demand for safe and reliable models. In this sense, this brief investigates the soft error reliability of two CNN inference models considering single event upsets (SEUs) occurring in register files, RAM, and Flash memory sections. The results show that the incidence of SEUs in flash memory sections tend to lead to more critical faults than those resulting from the occurrence of bit-flips in RAM sections and register files.