SERN: Modeling and Analyzing the Soft Error Reliability of Convolutional Neural Networks

Abstract

Convolutional Neural Networks (CNNs) are popular in artificial intelligence areas due to their high accuracy. Meanwhile, as manufacturing process technology scales, the probability of soft errors occurrence in computer systems increases, which causes reliability challenges for CNNs. For emerging safety and reliability critical CNN applications, such as autonomous vehicles, soft errors may cause catastrophic consequences. Thus it is important to analyze the vulnerability characteristics of CNNs. A common approach to analyze CNNs' reliability is fault injection, which requires a lot of computation resources and is time consuming. Thus a more efficient method is desired. In this work, we propose an analytical model named SERN to analyze the soft error reliability of CNNs, which requires only a small number of parameters in CNN models. Validation on several common CNN models shows SERN can efficiently and accurately perform soft error reliability analysis for CNNs. We also observe that the reliability of CNNs depend on data types, values, the sign of data and types of layers. Take advantage of these observations, we propose to protect vulnerable bits through ECC and protect error-prone layers through redundancy.