RakshithSuresh2001/ML-Based-Circuit-Performance-Prediction-for-VLSI-Design-Optimization
Developed ML model (Random Forest) to predict circuit delay/power from design parameters achieving R² > 0.95 on Nangate 45nm library, enabling 100× faster design space exploration vs. SPICE simulation
What it does
This repository features a machine learning model that predicts circuit delay and power consumption for VLSI design, achieving over 97% accuracy. It significantly accelerates design space exploration, making it 100 times faster than traditional SPICE simulations.
Star history
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Tracking
- Last trending
- 2026-02-21
Creator kit
Hook
Transform your VLSI design process with this ML model that predicts circuit performance 100x faster than SPICE!
Content angles
- Create a tutorial on how to implement the ML model for circuit performance prediction.
- Discuss the impact of machine learning on traditional VLSI design workflows.
- Analyze the results and performance metrics of the model compared to traditional methods.
Who should care
Engineers and researchers in VLSI design, machine learning enthusiasts, and data scientists.