# PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs

arXiv: https://arxiv.org/abs/2201.10114 · HTML: https://mlsanity.com/paper/2201.10114/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis](https://mlsanity.com/paper/2009.00871/) (2020)
- [An Ensemble Learning Approach for In-situ Monitoring of FPGA Dynamic Power](https://mlsanity.com/paper/2009.01432/) (2020)

## Built on by (most cited first, 10 of 10)

- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)
- [DeepSeq: Deep Sequential Circuit Learning](https://mlsanity.com/paper/2302.13608/) (2023)
- [DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations](https://mlsanity.com/paper/2411.00530/) (2024)
- [HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis](https://mlsanity.com/paper/2302.10977/) (2023)
- [FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2505.13461/) (2025)
- [ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis](https://mlsanity.com/paper/2507.03255/) (2025)
- [Iceberg: Enhancing HLS Modeling with Synthetic Data](https://mlsanity.com/paper/2507.09948/) (2025)
- [MPM-LLM4DSE: Reaching the Pareto Frontier in HLS with Multimodal Learning and LLM-Driven Exploration](https://mlsanity.com/paper/2601.04801/) (2026)
- [DiffHLS: Differential Learning for High-Level Synthesis QoR Prediction with GNNs and LLM Code Embeddings](https://mlsanity.com/paper/2604.09240/) (2026)
- [BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models](https://mlsanity.com/paper/2606.13747/) (2026)

## Abstract (arXiv)

Power estimation is the basis of many hardware optimization strategies. However, it is still challenging to offer accurate power estimation at an early stage such as high-level synthesis (HLS). In this paper, we propose PowerGear, a graph-learning-assisted power estimation approach for FPGA HLS, which features high accuracy, efficiency and transferability. PowerGear comprises two main components: a graph construction flow and a customized graph neural network (GNN) model. Specifically, in the graph construction flow, we introduce buffer insertion, datapath merging, graph trimming and feature annotation techniques to transform HLS designs into graph-structured data, which encode both intra-operation micro-architectures and inter-operation interconnects annotated with switching activities. Furthermore, we propose a novel power-aware heterogeneous edge-centric GNN model which effectively learns heterogeneous edge semantics and structural properties of the constructed graphs via edge-centric neighborhood aggregation, and fits the formulation of dynamic power. Compared with on-board measurement, PowerGear estimates total and dynamic power for new HLS designs with errors of 3.60% and 8.81%, respectively, which outperforms the prior arts in research and the commercial product Vivado. In addition, PowerGear demonstrates a speedup of 4x over Vivado power estimator. Finally, we present a case study in which PowerGear is exploited to facilitate design space exploration for FPGA HLS, leading to a performance gain of up to 11.2%, compared with methods using state-of-the-art predictive models.