Fast Switching Activity Estimation for HLS-Produced Dataflow Circuits

High-level synthesis (HLS) tools generate hardware designs from high-level software languages while sidestepping intricate low-level hardware details. However, HLS tools struggle with precise dynamic power estimation and optimization: the high abstraction level they operate on typically contains no or limited information on low-level circuit details that power consumption depends on. Dataflow circuits have recently been explored in the HLS context; apart from their ability to achieve performance that is superior to standard HLS-generated circuits, their well-defined structure and computational model offer entirely new opportunities for reasoning about power at the HLS level. This paper exploits this insight to present an accurate switching activity estimator for HLS-produced dataflow circuits. Our estimator combines the knowledge about the dataflow circuit structure with software profiling and detailed glitching analysis to estimate the circuit’s switching activity with an average error rate of 1.8% and average speedup of $17.8 \times$ compared with a cycle-accurate simulator. Our technology-agnostic solution makes a critical advancement in HLS power estimation and sets the stage for integrating power optimization within the HLS process.