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The paper introduces the Energy Flow Graph (EFG), a formal model representing software execution as a state-transition system with energy costs associated with states and transitions. EFG enables static analysis of energy-optimal execution paths and a multiplicative cascade model for predicting the combined effects of optimizations. Experiments across software programs and AI pipelines demonstrate EFG's ability to identify path-dependent energy variance and predict optimization combinations with high accuracy, leading to significant energy reductions.
Software energy consumption isn't just an aggregate number – it's a path-dependent journey, and this new model reveals hidden optimization opportunities that can slash energy use by up to 705x.
The growing energy demands of computational systems necessitate a fundamental shift from performance-centric design to one that treats energy consumption as one of the primary design considerations. Current approaches treat energy consumption as an aggregate, deterministic property, overlooking the path-dependent nature of computation, where different execution paths through the same software consume dramatically different energy. We introduce the Energy Flow Graph (EFG), a formal model that represents computational processes as state-transition systems with energy costs for both states and transitions. EFG enables various applications in software engineering, including static analysis of energy-optimal execution paths and a multiplicative cascade model that predicts combined optimization effects without exhaustive testing. Our early experiments demonstrate EFG's versatility across domains: in software programs validated through 3.5 million executions, 15.6% of solutions exhibit high path-dependent variance (CV $>$ 0.1), while structural optimization reveals up to 705$\times$ energy reduction. In AI pipelines, the cascade model predicts optimization combinations within 5.1% error, enabling selection from 4.2 million possibilities using only 22 measurements. The EFG transforms energy optimization from trial-and-error to systematic analysis, providing a foundation for green software engineering across computational domains.
