Saca-AVF: A Quantitative Approach to Analyze the Architectural Vulnerability Factors of CNN Accelerators

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.