Valid Application of EVT in Timing Analysis by Randomising Execution Time Measurements

Intrinsic timing uncertainties present in modern hardware platforms have motivated the use of Extreme Value Theory (EVT) to timing analysis, however, the timing behaviour of a task may not entirely fulfil the necessary assumptions. To deal with this difficulty, randomisation at the hardware level has been proposed as a means of facilitating the use of statistical timing analysis. However, it has been shown that hardware randomisation does not solve all the analysis problems and importantly some projects may not wish to change the hardware that is used to support timing analysis. This paper presents an innovative approach, which does not require hardware randomisation or any special system feature, named Indirect Estimation in Statistical Time Analysis (IESTA). The main difference is that randomised hardware is performed before software instructions actually executes and is applied to parameters (e.g. cache state) only indirectly linked to timing. In contrast, IESTA adds its randomisation directly to the timing measures without affecting the way the software is executed. The IESTA approach is evaluated by experiments on two real case studies for which execution time measurements are taken from an embedded platform and from a Rolls-Royce Full Authority Digital Engine Controller.