Automatic Identification of Timing Anomalies for Cycle-Accurate Worst-Case Execution Time Analysis

Hard real-time systems need methods to determine upper bounds for their execution times, usually called worst-case execution times. Timing anomalies are counterintuitive conditions in which a local speed-up of an instruction results in a global slow-down. Modern efficient timing analysis tools may yield inaccurate results when applied to processors with timing anomalies while methods which are suited for timing-anomalous systems are computationally expensive. Timing anomaly identification is key in choosing the right analysis technique for a given processor. In this paper, for the first time, an automated timing anomaly identification approach based on formal methods is presented. We validate the method by applying it to a simplified microprocessor using a commercial model checking tool.