# FirePower: Towards a Foundation with Generalizable Knowledge for Architecture-Level Power Modeling

arXiv: https://arxiv.org/abs/2410.17789 · HTML: https://mlsanity.com/paper/2410.17789/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [ArchPower: Dataset for Architecture-Level Power Modeling of Modern CPU Design](https://mlsanity.com/paper/2512.06854/) (2025)
- [BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models](https://mlsanity.com/paper/2606.13747/) (2026)

## Abstract (arXiv)

Power efficiency is a critical design objective in modern processor design. A high-fidelity architecture-level power modeling method is greatly needed by CPU architects for guiding early optimizations. However, traditional architecture-level power models can not meet the accuracy requirement, largely due to the discrepancy between the power model and actual design implementation. While some machine learning (ML)-based architecture-level power modeling methods have been proposed in recent years, the data-hungry ML model training process requires sufficient similar known designs, which are unrealistic in many development scenarios. This work proposes a new power modeling solution FirePower that targets few-shot learning scenario for new target architectures. FirePower proposes multiple new policies to utilize cross-architecture knowledge. First, it develops power models at component level, and components are defined in a power-friendly manner. Second, it supports different generalization strategies for models of different components. Third, it formulates generalizable and architecture-specific design knowledge into two separate models. FirePower also supports the evaluation of the generalization quality. In our experiments, FirePower can achieve a low error percentage of 5.8% and a high correlation R of 0.98 on average only using two configurations of target architecture. This is 8.8% lower in error percentage and 0.03 higher in R compared with directly training McPAT-Calib baseline on configurations of target architecture.