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This paper introduces a bio-inspired, closed-loop framework for energy-aware AI inference, drawing an analogy between protein-folding energy basins and inference cost landscapes. The framework uses a decaying threshold to admit inference requests based on utility-to-energy trade-offs, favoring local energy minima. Experiments using DistilBERT and ResNet-18 served via FastAPI, ONNX Runtime, and NVIDIA Triton demonstrate a 42% reduction in processing time with minimal accuracy loss compared to open-loop execution.
Mimicking protein folding can slash inference time by 42% while barely impacting accuracy, offering a new path to Green MLOps.
Energy efficiency is a first-order concern in AI deployment, as long-running inference can exceed training in cumulative carbon impact. We propose a bio-inspired framework that maps protein-folding energy basins to inference cost landscapes and controls execution via a decaying, closed-loop threshold. A request is admitted only when the expected utility-to-energy trade-off is favorable (high confidence/utility at low marginal energy and congestion), biasing operation toward the first acceptable local basin rather than pursuing costly global minima. We evaluate DistilBERT and ResNet-18 served through FastAPI with ONNX Runtime and NVIDIA Triton on an RTX 4000 Ada GPU. Our ablation study reveals that the bio-controller reduces processing time by 42% compared to standard open-loop execution (0.50s vs 0.29s on A100 test set), with a minimal accuracy degradation (<0.5%). Furthermore, we establish the efficiency boundaries between lightweight local serving (ORT) and managed batching (Triton). The results connect biophysical energy models to Green MLOps and offer a practical, auditable basis for closed-loop energy-aware inference in production.
