Convolutional neural network (CNN) accelerators are widely used in artificial intelligence applications, such as image recognitions, due to their superior computing performance. However, as manufacturing technology scales down, the shrinking of chip size and the increasing of integration density make CNN accelerators vulnerable to soft errors, which are key factors affecting the reliability of integrated circuits. For emerging CNN applications where reliability is critical (such as self-driving cars), visible faults in applications’ outputs caused by soft errors may lead to catastrophic consequences. Therefore, it is important to consider reliability into CNN accelerators’ architecture design. Recent modeling and fault injection approaches analyze the reliability features of CNN models, but none evaluate the soft error reliability of CNN accelerators from architecture level. The Architectural Vulnerability Factor (AVF) indicates the probability that a soft error results in faulty computation outcomes. The Architecturally Correct Execution (ACE) method analyzes AVF of structures by identifying ACE bits and calculating their residency time. Based on the traditional ACE-based AVF analysis model, we propose a quantitative approach saca-AVF to analyze the AVF of systolic array based CNN accelerators that perform homogeneous matrix arithmetic operations instead of various kinds of instructions. We explore the reliability features of CNN accelerators under different design choices at different levels leveraging saca-AVF. We also observe several reliability characteristics of CNN accelerator architecture. We believe our proposed saca-AVF and the observations we made are able to guide designers to discover the reliability hotspots of CNN accelerators and build highly reliable CNN accelerators.

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.