# PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network

arXiv: https://arxiv.org/abs/2011.13494 · HTML: https://mlsanity.com/paper/2011.13494/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [AI/ML Algorithms and Applications in VLSI Design and Technology](https://mlsanity.com/paper/2202.10015/) (2022)
- [PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction](https://mlsanity.com/paper/2403.18569/) (2024)
- [A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation](https://mlsanity.com/paper/2204.09579/) (2022)
- [CFIRSTNET: Comprehensive Features for Static IR Drop Estimation with Neural Network](https://mlsanity.com/paper/2502.12168/) (2025)
- [GIF: A Conditional Multimodal Generative Framework for IR Drop Imaging in Chip Layouts](https://mlsanity.com/paper/2604.09999/) (2026)
- [Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networks](https://mlsanity.com/paper/2110.14197/) (2021)
- [The Dark Side: Security Concerns in Machine Learning for EDA](https://mlsanity.com/paper/2203.10597/) (2022)
- [Worst-Case Dynamic Power Distribution Network Noise Prediction Using Convolutional Neural Network](https://mlsanity.com/paper/2204.13109/) (2022)
- [Intelligent Circuit Design and Implementation with Machine Learning](https://mlsanity.com/paper/2206.03032/) (2022)
- [Static IR Drop Prediction with Attention U-Net and Saliency-Based Explainability](https://mlsanity.com/paper/2408.03292/) (2024)
- [Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate](https://mlsanity.com/paper/2502.05345/) (2025)
- [DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Design](https://mlsanity.com/paper/2507.10606/) (2025)
- [WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Design](https://mlsanity.com/paper/2507.19197/) (2025)
- [LMM-IR: Large-Scale Netlist-Aware Multimodal Framework for Static IR-Drop Prediction](https://mlsanity.com/paper/2511.12581/) (2025)
- [PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay](https://mlsanity.com/paper/2605.10547/) (2026)

## Abstract (arXiv)

IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violations. In this work, we develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN). It can handle both vector-based and vectorless IR analyses. Moreover, the proposed CNN model is general and transferable to different designs. This is in contrast to most existing machine learning (ML) approaches, where a model is applicable only to a specific design. Experimental results show that PowerNet outperforms the latest ML method by 9% in accuracy for the challenging case of vectorless IR drop and achieves a 30 times speedup compared to an accurate IR drop commercial tool. Further, a mitigation tool guided by PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs, respectively, with very limited modification on their power grids.