Search papers, labs, and topics across Lattice.
EnergAIzer tackles the scalability bottleneck in GPU power estimation for AI workloads by predicting hardware utilization using analytical models based on structured patterns in kernel optimizations. It constructs a performance model using these patterns as an analytical scaffold for empirical data fitting, enabling module-level utilization prediction. EnergAIzer achieves 8% power estimation errors on NVIDIA Ampere GPUs and 7% on NVIDIA H100, significantly reducing estimation time from hours to seconds.
Forget hours-long simulations: EnergAIzer slashes GPU power estimation time to seconds while maintaining accuracy, by exploiting structured patterns in AI kernel optimizations.
As AI workloads drive increases in datacenter power consumption, accurate GPU power estimation is critical for proactive power management. However, existing power models face a scalability bottleneck not in the modeling techniques themselves, but in obtaining the hardware utilization inputs they require. Conventional approaches rely on either costly simulation or hardware profiling, which makes them impractical when rapid predictions are required. This work presents EnergAIzer, which addresses this scalability bottleneck by developing a lightweight solution to predict utilization inputs, reducing the estimation walltime from hours to seconds. Our key insight is that kernels in AI workloads commonly employ optimizations that create structured patterns, which analytically determine memory traffic and execution timeline. We construct a performance model using these patterns as an analytical scaffold for empirical data fitting, which also naturally exposes module-level utilization. This predicted utilization is then fed into our power model to estimate dynamic power consumption. EnergAIzer achieves 8% power errors on NVIDIA Ampere GPUs, competitive with traditional power models with elaborate cycle-level simulation or hardware profiling. We demonstrate EnergAIzer's exploration capabilities for frequency scaling and architectural configurations, including forecasting the power of NVIDIA H100 with just 7% error. In summary, EnergAIzer provides fast and accurate power prediction for AI workloads, paving the way for power-aware design explorations.
