Machine Learning Based Fast Power Integrity Classifier

TL;DR

This paper introduces a machine learning-based classifier that rapidly detects power integrity issues like EM/IR hotspots, utilizing features from power grids and comparing multiple models for optimal performance.

Contribution

It presents a novel approach combining feature extraction and machine learning models to efficiently identify power integrity problems in integrated circuits.

Findings

Random forest achieved the best accuracy.

The classifier effectively distinguishes continuous and discontinuous cases.

Promising results on open source benchmarks.

Abstract

In this paper, we proposed a new machine learning based fast power integrity classifier that quickly flags the EM/IR hotspots. We discussed the features to extract to describe the power grid, cell power density, routing impact and controlled collapse chip connection (C4) bumps, etc. The continuous and discontinuous cases are identified and treated using different machine learning models. Nearest neighbors, random forest and neural network models are compared to select the best performance candidates. Experiments are run on open source benchmark, and result is showing promising prediction accuracy.