Deploying Convolutional Neural Networks (CNNs) on edge platforms necessitates efficient hardware acceleration. Any unnecessary data movement in such accelerators can unacceptably degrade performance and efficiency. To address this, we develop a layer fusion technique targeting CNNs, that reduces off-chip data communication using a Genetic Algorithm (GA) applied to graph-based topological sort. Results show a 1.8× increase in energy efficiency and 1.9× improvement in energy-delay product (EDP) for MobileNet-v3 on a SIMBA-like mobile architecture. Our approach consistently improves workload performance, averaging 1.4× improvement to EDP for SIMBA and 1.12× for Eyeriss.
@article{arxiv.2311.12235,
title = {Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic Algorithms},
author = {Mark Horeni and Siddharth Joshi},
journal= {arXiv preprint arXiv:2311.12235},
year = {2023}
}