EvoML-EDA: Synergy of Evolutionary Algorithms and Advanced Machine Learning Algorithms for Digital Circuit Design

This project develops a novel hybrid framework integrating evolutionary
algorithms with advanced machine learning models to revolutionize digital circuit
design. Current methodologies face significant challenges: evolutionary
approaches suffer from inefficient blind operators and expensive evaluations,
while ML-based approaches lack hardware-specific training data. Our framework
addresses these limitations through ML-guided evolutionary operators,
structure-aware surrogate models, and specialized techniques for emerging
technologies and verification requirements. The synergistic combination leverages
evolutionary algorithms' exploration capabilities with ML's pattern recognition
power. We will validate our approach through diverse case studies including
approximate accelerators, verification-optimized designs, medical signal
classifiers, and superconducting circuits, advancing automated circuit design for
both current and emerging technologies.