The use of intelligent data analysis techniques for system-level design: a software estimation example

Abstract

System-level designers typically rely on white-box detailed descriptions of embedded systems in order to perform their design choices and optimisations. The white-box approach assumes that the complexity of the system can be managed by decomposing the global system into a set of simple and well-described components. This assumption is generally not true when non-linear interactions happen between the different subsystems. This paper explores, as an alternative, a black-box modelling methodology based on intelligent data analysis in order to support system-level designers in characterising the performance of embedded applications. The rationale behind this approach is that nowadays the designer can easily store large amount of data related to the system, and consequently discover relevant information by analysing them in a black-box fashion. As a case study, we address the performance modelling of software applications running on an embedded microprocessor. We introduce a data analysis method which, on the basis of a high-level characterisation of the software functionality and the hardware architecture, is able to predict the number of execution cycles on a embedded processor. We propose the adoption of a local learning technique (lazy learning), which proved already to be effective in previous works, to model the unknown input/output relation between the hardware/software parameters of the application and the number of execution cycles. Experiments with standard computational code (sorting, mathematical computation) and with a specific streaming algorithm for MPEG variable length decoding are presented to support this claim.