# MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification

arXiv: https://arxiv.org/abs/2012.10597 · HTML: https://mlsanity.com/paper/2012.10597/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2020.

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

- [CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)](https://mlsanity.com/paper/2208.01040/) (2022)
- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)
- [PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction](https://mlsanity.com/paper/2403.18569/) (2024)
- [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)
- [On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations](https://mlsanity.com/paper/2403.00103/) (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)
- [Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation](https://mlsanity.com/paper/2605.08291/) (2026)

## Abstract (arXiv)

Vectored IR drop analysis is a critical step in chip signoff that checks the power integrity of an on-chip power delivery network. Due to the prohibitive runtimes of dynamic IR drop analysis, the large number of test patterns must be whittled down to a small subset of worst-case IR vectors. Unlike the traditional slow heuristic method that select a few vectors with incomplete coverage, MAVIREC uses machine learning techniques -- 3D convolutions and regression-like layers -- for accurately recommending a larger subset of test patterns that exercise worst-case scenarios. In under 30 minutes, MAVIREC profiles 100K-cycle vectors and provides better coverage than a state-of-the-art industrial flow. Further, MAVIREC's IR drop predictor shows 10x speedup with under 4mV RMSE relative to an industrial flow.