Unleashing Flexibility of ML-based Power Estimators Through Efficient Development Strategies
Yao Lu, Qijun Zhang, Zhiyao Xie · 2024
Power is a primary design objective in modern VLSI design. Efficient and accurate power evaluation tools are in high demand to provide prompt power feedback for early design optimization. However, it is time-consuming to simulate long, fine-grained (e.g., per-cycle) power traces in complex designs with commercial power simulators. In recent years, machine learning (ML)-based power models have emerged as a potential solution to make fast predictions on per-cycle power based on signal toggling activities. Despite their immense potential, these models are currently underutilized in realistic development scenarios, largely due to the challenges associated with their development and updates. To overcome the barriers, this work optimizes the often-neglected power model development process by an in-depth examination of power data's impact on model accuracy. We propose efficient strategies to minimize the overhead involved in model development. Furthermore, we enhance model flexibility by enabling the transfer of existing models to updated design RTLs with negligible additional costs.