GreenSafe-LLM: Energy-Aware Safety Optimization for Large Foundation Models via Selective Computational Path Intervention
Keywords:
energy-aware optimization, large foundation models, safety alignment, selective path intervention, computational efficiency, carbon-aware AI, model governanceAbstract
The deployment of large foundation models, such as transformer-based language and vision systems, has introduced unprecedented capabilities in natural language understanding, generation, and multimodal reasoning. However, these models incur substantial operational energy costs and present significant safety challenges, including the generation of harmful, biased, or factually inaccurate content. Existing safety alignment methods often impose uniform computational overhead across all inference paths, disregarding the heterogeneity of risk and the varying energy consumption of different internal computations. This paper introduces GreenSafe-LLM, a system-level framework that simultaneously optimizes for energy efficiency and safety by selectively intervening on computational paths during inference. GreenSafe-LLM integrates a lightweight risk estimator that dynamically identifies high-risk pathways, a set of targeted intervention modules that modify only those pathways, and an energy-aware scheduler that balances safety gains against per-query energy budgets. The architecture leverages sparse activation patterns and early-exit mechanisms to reduce total floating-point operations while preserving alignment with human values and regulatory requirements. We discuss structural trade-offs between intervention granularity, latency, and carbon footprint, and examine governance implications for deploying such systems in large-scale cloud environments and edge devices. Through conceptual analysis and cross-domain comparisons with prior work on pruning, mixture-of-experts, and path-level safety intervention, we argue that selective computational path intervention offers a tractable middle ground between brute-force safety alignment and unrestrained generation. The paper concludes with forward-looking perspectives on policy frameworks that reward energy-aware safety optimization and the integration of real-time carbon intensity signals into model serving infrastructure.
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