CompressNAS : A Fast and Efficient Technique for Model Compression using Decomposition

• URL: http://arxiv.org/abs/2511.11716v1
• Date: Wed, 12 Nov 2025 18:25:46 GMT
• Title: CompressNAS : A Fast and Efficient Technique for Model Compression using Decomposition
• Authors: Sudhakar Sah, Nikhil Chabbra, Matthieu Durnerin,
• Abstract summary: We introduce CompressNAS, a framework that treats rank selection as a global search problem.<n>In ImageNet, CompressNAS compresses ResNet-18 by 8x with less than 4% accuracy drop; on COCO, we achieve 2x compression of YOLOv5s without any accuracy drop.<n>We present a new family of compressed models, STResNet, with competitive performance compared to other efficient models.
• Score: 1.9556774372563988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Convolutional Neural Networks (CNNs) are increasingly difficult to deploy on microcontrollers (MCUs) and lightweight NPUs (Neural Processing Units) due to their growing size and compute demands. Low-rank tensor decomposition, such as Tucker factorization, is a promising way to reduce parameters and operations with reasonable accuracy loss. However, existing approaches select ranks locally and often ignore global trade-offs between compression and accuracy. We introduce CompressNAS, a MicroNAS-inspired framework that treats rank selection as a global search problem. CompressNAS employs a fast accuracy estimator to evaluate candidate decompositions, enabling efficient yet exhaustive rank exploration under memory and accuracy constraints. In ImageNet, CompressNAS compresses ResNet-18 by 8x with less than 4% accuracy drop; on COCO, we achieve 2x compression of YOLOv5s without any accuracy drop and 2x compression of YOLOv5n with a 2.5% drop. Finally, we present a new family of compressed models, STResNet, with competitive performance compared to other efficient models.

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