# Analytical Characterization and Design Space Exploration for Optimization of CNNs

arXiv: https://arxiv.org/abs/2101.09808 · HTML: https://mlsanity.com/paper/2101.09808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2021.

## Abstract (arXiv)

Moving data through the memory hierarchy is a fundamental bottleneck that can limit the performance of core algorithms of machine learning, such as convolutional neural networks (CNNs). Loop-level optimization, including loop tiling and loop permutation, are fundamental transformations to reduce data movement. However, the search space for finding the best loop-level optimization configuration is explosively large. This paper develops an analytical modeling approach for finding the best loop-level optimization configuration for CNNs on multi-core CPUs. Experimental evaluation shows that this approach achieves comparable or better performance than state-of-the-art libraries and auto-tuning based optimizers for CNNs.