ESL power estimation using virtual platforms with black box processor models

Abstract

Processor models for electronic system level (ESL) simulations are usually provided by their vendors as binary object code. Those binaries appear as black boxes, which do not allow to observe their internals. This prevents the application of most existing ESL power estimation methodologies. To remedy this situation, this work presents an estimation methodology for the case of black box models. The evaluation for the ARM Cortex-A9 processor shows that the proposed approach is able to achieve a high accuracy. In comparison to hardware power measurements obtained from the OMAP4460 chip on the PandaBoard, the ESL estimation error is below 5%.