<b>Addressing power efficiency challenges in AI hardware through verification </b>

AI accelerators already run with constrained energy and thermal budgets, and small inefficiencies are amplified across an entire fleet, resulting in increased costs and emissions. This work redefines power efficiency as a checkable requirement and not a back-silicon addition. It specifies power intent in IEEE 1801 (UPF), encodes protocol noncorrectness with SystemVerilog/PSL assertions, and quantifies progress with power-state, transition, and cross coverage (DVFS X workload phase X thermal bin). The reproducible dataset schema integrates time with microarchitectural counters, voltage, frequency, temperature, and power, measured across real workloads (ResNet, BERT, and attention/GEMM) in simulation, emulation, and instrumented silicon. Telemetry input is synchronised using triggers and PTP/NTP; rails calibrated and error budgets quoted. The continuous integration gates are merged on quantitative thresholds (e.g., >2% p95 energy/inference regression), and dashboards auto-bisect offending changes. Experiments show a hybrid analytical-plus-ML estimator of 3.8-6.1% MAPE at millisecond latency with 30- 60x emulation throughput compared to simulation and mid-single-digit energy reductions due to verification-driven fixes. Case studies involve preventing standby leakage through restored isolation, smoothing a DVFS table to eliminate 10-15 ms oscillations, and fixing compiler schedules that caused incorrect L2 miss models and increased DRAM data traffic. This yields a realistic, start-to-finish pipeline UPF, ABV/formal, emulation/FPGA, calibrated rigs, and CI to bring watts into the top echelon of test metrics and achieve long-lasting efficiency improvements in GPU/NPU/ASIC accelerators. The full scope includes training and inference across 14-5 nm nodes adhering to rigorous safety, ethics, and licensing practices.