Contention-Aware Forecasting of Energy Efficiency through Sequence-Based Models in Modern Heterogeneous Processors
Mohammed Bakr Sikal, Jeferson González-Gómez, Heba Khdr, Jörg Henkel · 2025
We present EffiCast, the first methodology for contentionaware energy efficiency forecasting in clustered heterogeneous processors using sequence-based models. Through extensive experimental analysis of energy efficiency sensitivities across core types, voltage/frequency (V/f) levels, application phases, and resource contention scenarios, EffiCast uncovers key factors driving energy efficiency variability in modern heterogeneous processors. Leveraging structured data generation and advanced LSTM- and Transformer-based models, EffiCast achieves unprecedented accuracy while outperforming state-of-the-art predictive techniques. Deployed on a real heterogenous processor with Intel’s oneDNN acceleration, EffiCast delivers inference latencies as low as 1.82 ms per sequence, enabling seamless integration into proactive resource management frameworks. With the ability to forecast future system states under dynamic workloads, EffiCast sets a new standard for energy efficiency optimization in energy-constrained application domains.