Breaking the I/O Crisis: An AI-Driven Selective Emulation Framework for 500x Data Reduction in GPU Verification

Modern GPU subsystem verification is severely bottlenecked by the latency of hardware emulation, where full-frame benchmark simulations generate waveform data in order of terabytes and require hours of wall time to render the whole frame. This paper proposes a tool-agnostic AI framework designed to decouple predictive accuracy from raw data volume. By intelligently identifying critical temporal windows and spatial hotspots, the methodology enables high-fidelity power and performance prediction using only a representative sample of switching activity. Our results demonstrate that this framework maintains high accuracy with less than 1% power prediction error while achieving a transformative 100x to 500x reduction in data footprint. This approach significantly accelerates the Power-Performance-Area (PPA) optimization loop within industry-standard verification environments.