This work presents a symbolic approach for estimating the energy consumption for nested loop programs when mapped and scheduled on parallel processor array accelerator architectures. Instead of simulation-based evaluation, we derive a methodology for symbolic energy analysis that captures the impact of mapping and scheduling decisions of loop nests on processor arrays. We compare our approach against simulation-based results for selected benchmarks and varying sizes of the iteration spaces. Whereas the latter are not scalable, our symbolic analysis is shown to be independent of the problem size. The presented evaluation methodology can be beneficially used during the design space exploration of mapping and scheduling decisions, for studying the influence of array size variations, and for comparisons with other loop nest accelerator architectures.
@article{arxiv.2604.07287,
title = {Symbolic Polyhedral-Based Energy Analysis for Nested Loop Programs},
author = {Avinash Mahesh Nirmala and Dominik Walter and Frank Hannig and Jürgen Teich},
journal= {arXiv preprint arXiv:2604.07287},
year = {2026}
}