Cross-Platform Power Prediction for Heterogeneous Accelerators: An Architecture-Aware Transfer Learning Approach

Run-time power prediction on heterogeneous accelerators underpins energy-aware scheduling, thermal throttling, and capacity planning. Conventional counter-based regression models must be rebuilt for every new platform, delaying deployment. We present an architecture-aware transfer learning framework that recycles knowledge from a data-rich source GPU to predict power on an unseen target from a small labelled subset. The core idea encodes raw counters into a shared representation of normalized utilization signals and explicit architectural descriptors, making the two feature spaces directly comparable. A base predictor trained on the source is then adapted through a lightweight architecture-gated residual. On a V100 source and three targets (RTX 2080 Ti, Tesla T4, A100) spanning Turing and Ampere, the method attains with only 10% of the target data a mean absolute percentage error (MAPE) of 7.8% averaged over the three transfer targets (7.7% including the source)—cutting the error of direct transfer by 52.9% on those targets (95% CI [47.4%, 58.3%]) and approaching the full-data oracle. Cross-platform power models can thus be built economically by reusing architectural knowledge.