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 .
machine-learning random-forest power-gating vlsi-design digital-circuit-optimization decoder-design low-power-electronics ppa-optimization
-
Updated
Apr 24, 2026 - Python