An Adaptive Control Framework for Sustainable Edge Inference of Cross-Modal Foundation Models

Ali Hussein Khalaf AL-Sammarraie

doi:10.71229/zfs6t418

Authors

Ali Hussein Khalaf AL-Sammarraie Ministry of Education

DOI:

https://doi.org/10.71229/zfs6t418

Keywords:

Green Information Systems, Edge Intelligence, Foundation Models, Dynamic Token Pruning, Sustainable AI

Abstract

Running cross-modal Foundation Models (FMs) near to the edge unlocks innovative IoT use-cases but comes with unmanageable energy and carbon emissions costs. Static optimization techniques completely break under dynamic edge constraints, and throw away all of these precious compute resources while generating inference pipelines that are not truly sustainable. We propose a closed-loop telemetry- driven control architecture, called the Adaptive Sustainable Framework (ASF), to adaptively vary token pruning intensity as function of real-time battery/thermal/network conditions. Tested through Design Science Research over a variety of edge devices (NVIDIA Jetson, Raspberry Pi) and cross-modal datasets, ASF mitigates up to 56% in energy use - and thus proportional carbon emissions - whilst maintaining the task accuracy to within 3% of the unoptimized baseline number. Our framework achieves 15–32% better energy efficiency compared to static pruning, quantization-only, or adaptive offloading baselines across non-stationary workloads. This work contributes to Green Information Systems (Green IS) theory by exhibiting how sustainability can be operationalized as a runtime control target, enriches Design Science methodology with carbon-aware design criteria and provides IT practitioners with actionable deployment guidelines that align the metrics of net-talk time & bandwidth usage in regard to regulatory compliance obligations for the ESG-space. We show that it is system-level adaptivity and not algorithmic novelty, which enables sustainable edge intelligence.

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