Devanik21/A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI
The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
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Language
Python
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MIT
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Last pushed
Mar 20, 2026
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