Dynamic Power and Performance Back-Annotation for Fast and Accurate Functional Hardware Simulation

Abstract

Virtual platform prototypes are widely used for early design space exploration at the system level. There is, however, a lack of accurate and fast power and performance models of hardware components at such high levels of abstraction. In this paper, we present an approach that extends fast functional hardware models with the ability to produce detailed, cycle-level timing and power estimates. Our approach is based on back-annotating behavioral hardware descriptions with a dynamic power and performance model that allows capturing cycle-accurate and data-dependent activity without a significant loss in simulation speed. By integrating with existing high-level synthesis (HLS) flows, back-annotation is fully automated for custom hardware synthesized by HLS. We further leverage state-of-the-art machine learning techniques to synthesize abstract power models, where we introduce a structural decomposition technique to reduce model complexities and increase estimation accuracy. We have applied our back-annotation approach to several industrial-strength design examples under various architecture configurations. Results show that our models predict average power consumption to within 1% and cycle-by-cycle power dissipation to within 10% of a commercial gate-level power estimation tool, all while running several orders of magnitude faster.