Worst Case Execution Time and Power Estimation of Multicore and GPU Software: A Pedestrian Detection Use Case

Abstract

Worst Case Execution Time estimation of software running on parallel platforms is a challenging task, due to resource interference of other tasks and the complexity of the underlying CPU and GPU hardware architectures. Similarly, the increased complexity of the hardware, challenges the estimation of worst case power consumption. In this paper, we employ Measurement Based Probabilistic Timing Analysis (MBPTA), which is capable of managing complex architectures such as multicores. We enable its use by software randomisation, which we show for the first time that is also possible on GPUs. We demonstrate our method on a pedestrian detection use case on an embedded multicore and GPU platform for the automotive domain, the NVIDIA Xavier. Moreover, we extend our measurement based probabilistic method in order to predict the worst case power consumption of the software on the same platform.