In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, architecture, and systems-to optimize the deployment of machine learning workloads on analog processing-in-memory (PIM) systems. CrossNAS leverages the single-path one-shot weight-sharing strategy combined with the evolutionary search for the first time in the context of PIM system mapping and optimization. CrossNAS sets a new benchmark for PIM neural architecture search (NAS), outperforming previous methods in both accuracy and energy efficiency while maintaining comparable or shorter search times.
@article{arxiv.2505.22868,
title = {CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems},
author = {Md Hasibul Amin and Mohammadreza Mohammadi and Jason D. Bakos and Ramtin Zand},
journal= {arXiv preprint arXiv:2505.22868},
year = {2025}
}