Search papers, labs, and topics across Lattice.
This paper introduces X-OPM, a novel explainable on-chip power modeling (OPM) framework designed to enhance the robustness of proactive power management systems by improving runtime power prediction accuracy. X-OPM leverages a feature engineering approach with tree-based models for interaction capture and linear models for prediction, grounded in VLSI design principles, and incorporates a human-in-the-loop workflow. Evaluated on a C906 vector processor, X-OPM achieves $R^2>0.93$ across diverse workloads with minimal area overhead (<0.1%), outperforming existing state-of-the-art methods in generalization.
A novel explainable power modeling framework achieves state-of-the-art prediction accuracy and generalization on diverse workloads with negligible hardware overhead, unlike opaque black-box models.
Proactive power management systems reduce processor dynamic power through runtime power prediction and power-aware scheduling. Accurate, stable and low-overhead digital on-chip power meters (OPMs) are crucial for improving the prediction quality. Recent studies have explored various modeling methods, including using linear models, decision trees, and multi-layer perceptrons (MLPs) to construct OPMs. However, most current approaches train models end-to-end without analyzing the physical interpretability of features, affecting their ability to generalize to unseen workloads. Grounded in the design principles of synchronous digital VLSI circuits, X-OPM introduces a robust feature engineering framework that uses tree-based models to capture feature interactions and linear models for prediction. It also incorporates a human-in-the-loop workflow to balance model accuracy against modeling effort. Evaluated on a commercial C906 vector processor, X-OPM consistently achieves $R^2>0.93$ across all workloads with sampling window size set below $8$ cycles. In contrast, state-of-the-art methods including APOLLO, COBIT, and standard MLPs fail to generalize across all test cases. Layout with commercial EDA tools shows that X-OPM incurs an area overhead below $0.1\%$, which is on par with lightweight tree-based and linear models, and significantly smaller than MLP-based models.
