# OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis

arXiv: https://arxiv.org/abs/2110.14184 · HTML: https://mlsanity.com/paper/2110.14184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks](https://mlsanity.com/paper/2009.09009/) (2020)
- [Simulation Algorithms with Exponential Integration for Time-Domain Analysis of Large-Scale Power Delivery Networks](https://mlsanity.com/paper/1505.06699/) (2015)
- [PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning](https://mlsanity.com/paper/2005.01386/) (2020)

## Built on by (most cited first, 1 of 1)

- [WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Design](https://mlsanity.com/paper/2507.19197/) (2025)

## Abstract (arXiv)

Power delivery network (PDN) design is a nontrivial, time-intensive, and iterative task. Correct PDN design must account for considerations related to power bumps, currents, blockages, and signal congestion distribution patterns. This work proposes a machine learning-based methodology that employs a set of predefined PDN templates. At the floorplan stage, coarse estimates of current, congestion, macro/blockages, and C4 bump distributions are used to synthesize a grid for early design. At the placement stage, the grid is incrementally refined based on more accurate and fine-grained distributions of current and congestion. At each stage, a convolutional neural network (CNN) selects an appropriate PDN template for each region on the chip, building a safe-by-construction PDN that meets IR drop and electromigration (EM) specifications. The CNN is initially trained using a large synthetically-created dataset, following which transfer learning is leveraged to bridge the gap between real-circuit data (with a limited dataset size) and synthetically-generated data. On average, the optimization of the PDN frees thousands of routing tracks in congestion-critical regions, when compared to a globally uniform PDN, while staying within the IR drop and EM limits.