mlsanityData to 7 Oct 2026

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

arXiv 2011.13494 ↗First posted 26 Nov 2020cs.LG cs.ARcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 0; 2024: 2; 2025: 5; 2026: 2.

16 papers.

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  1. 2021Machine Learning for Electronic Design Automation: A Surveycited by 71
  2. 2022AI/ML Algorithms and Applications in VLSI Design and Technologycited by 5
  3. 2024PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Predictioncited by 3
  4. 2022A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automationcited by 1
  5. 2025CFIRSTNET: Comprehensive Features for Static IR Drop Estimation with Neural Networkcited by 1
  6. 2026GIF: A Conditional Multimodal Generative Framework for IR Drop Imaging in Chip Layoutscited by 1
  7. 2021Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networkscited by 0
  8. 2022The Dark Side: Security Concerns in Machine Learning for EDAcited by 0
  9. 2022Worst-Case Dynamic Power Distribution Network Noise Prediction Using Convolutional Neural Networkcited by 0
  10. 2022Intelligent Circuit Design and Implementation with Machine Learningcited by 0
  11. 2024Static IR Drop Prediction with Attention U-Net and Saliency-Based Explainabilitycited by 0
  12. 2025Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogatecited by 0
4 more
  1. 2025DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Designcited by 0
  2. 2025WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Designcited by 0
  3. 2025LMM-IR: Large-Scale Netlist-Aware Multimodal Framework for Static IR-Drop Predictioncited by 0
  4. 2026PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decaycited by 0