Utilising Assumptions to Determine the WCET of Multi-component Classification Systems

Cyber-Physical Systems (CPS) are being used increasingly in safety-critical settings. These are often in complex and dynamic environments that require a range of sensors and AI-enabled classifiers to be employed to monitor and understand the challenges posed by such environments. For real-time CPS it is necessary to execute a range of classifiers so that: (i) the final output is delivered on time, (ii) the required level of confidence in the correctness of this output is beyond a safety-derived threshold, and (iii) the minimum level of resources is consumed in the process of meeting these timing and confidence constraints. To undertake timing analysis the Worst-Case Execution Time (WCET) of these multi-component classification systems must be estimated; doing so requires the maximum input load imposed by any potential environment to be determined. In this paper we show how this can be done by exploiting well-defined assumptions about the environment via a Dynamic Programming algorithm. Assumptions can take many forms; here we demonstrate the expressive power of the approach by including a range of illustrative examples.