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The ever increasing number and complexity of energy-bound devices (such asthe ones used in Internet of Things applications, smart phones, and missioncritical systems) pose an important challenge on techniques to optimize theirenergy consumption and to verify that they will perform their function withinthe available energy budget. In this work we address this challenge from thesoftware point of view and propose a novel parametric approach to estimatingtight bounds on the energy consumed by program executions that are practicalfor their application to energy verification and optimization. Our approachdivides a program into basic (branchless) blocks and estimates the maximal andminimal energy consumption for each block using an evolutionary algorithm. Thenit combines the obtained values according to the program control flow, usingstatic analysis, to infer functions that give both upper and lower bounds onthe energy consumption of the whole program and its procedures as functions oninput data sizes. We have tested our approach on (C-like) embedded programsrunning on the XMOS hardware platform. However, our method is general enough tobe applied to other microprocessor architectures and programming languages. Thebounds obtained by our prototype implementation can be tight while remaining onthe safe side of budgets in practice, as shown by our experimental evaluation.

Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks