Convolutional neural networks (CNNs) require a large number of multiply-accumulate (MAC) operations. To meet real-time constraints, they often need to be executed on specialized accelerators composed of an on-chip memory and a processing unit. However, the on-chip memory is often insufficient to store all the data required to compute a CNN layer. Thus, the computation must be performed in several offloading steps. We formalise such sequences of steps and apply our formalism to a state of the art decomposition of convolutions. In order to find optimal strategies in terms of duration, we encode the problem with a set of constraints. A Python-based simulator allows to analyse in-depth computed strategies.
@article{arxiv.2603.21792,
title = {Convolutions Predictable Offloading to an Accelerator: Formalization and Optimization},
author = {Benjamin Husson and Mohammed Belcaïd and Thomas Carle and Claire Pagetti},
journal= {arXiv preprint arXiv:2603.21792},
year = {2026}
}