[
    {
        "id": "osp-15535",
        "type": "article-journal",
        "title": "X-OPM: Explainable Automatic Digital On-Chip Power Modeling for Enhanced Robustness",
        "author": [
            {
                "family": "Jiang",
                "given": "Jingbo"
            },
            {
                "family": "Chen",
                "given": "Xizi"
            },
            {
                "family": "Peng",
                "given": "Jian"
            },
            {
                "family": "Zhang",
                "given": "Wei"
            }
        ],
        "URL": "https://omanscience.com/en/articles/x-opm-explainable-automatic-digital-on-chip-power-modeling-for-enhanced-robustness",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]