# Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networks

arXiv: https://arxiv.org/abs/2110.14197 · HTML: https://mlsanity.com/paper/2110.14197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network](https://mlsanity.com/paper/2011.13494/) (2020)
- [Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks](https://mlsanity.com/paper/2009.09009/) (2020)
- [Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks](https://mlsanity.com/paper/2002.00367/) (2020)
- [MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification](https://mlsanity.com/paper/2012.10597/) (2020)
- [A New, Computationally Efficient "Blech Criterion" for Immortality in General Interconnects](https://mlsanity.com/paper/2105.08784/) (2021)

## Abstract (arXiv)

Power delivery network (PDN) analysis and thermal analysis are computationally expensive tasks that are essential for successful IC design. Algorithmically, both these analyses have similar computational structure and complexity as they involve the solution to a partial differential equation of the same form. This paper converts these analyses into image-to-image and sequence-to-sequence translation tasks, which allows leveraging a class of machine learning models with an encoder-decoder-based generative (EDGe) architecture to address the time-intensive nature of these tasks. For PDN analysis, we propose two networks: (i) IREDGe: a full-chip static and dynamic IR drop predictor and (ii) EMEDGe: electromigration (EM) hotspot classifier based on input power, power grid distribution, and power pad distribution patterns. For thermal analysis, we propose ThermEDGe, a full-chip static and dynamic temperature estimator based on input power distribution patterns for thermal analysis. These networks are transferable across designs synthesized within the same technology and packing solution. The networks predict on-chip IR drop, EM hotspot locations, and temperature in milliseconds with negligibly small errors against commercial tools requiring several hours.