# Mix-and-Match Pruning: Globally Guided Layer-Wise Sparsification of DNNs

Source: [Mix-and-Match Pruning: Globally Guided Layer-Wise Sparsification of DNNs](<https://www.sota2.com/research/paper/69c21a870636e5bf4afc47f4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-25T05:00:04.214Z

Authors: Danial Monachan, Samira Nazari, Mahdi Taheri, Ali Azarpeyvand, Milos Krstic, Michael Huebner, Christian Herglotz

Published: 2026-03-17T23:27:07.000Z

## Abstract

Deploying deep neural networks (DNNs) on edge devices requires strong compression with minimal accuracy loss. This paper introduces Mix-and-Match Pruning, a globally guided, layer-wise sparsification framework that leverages sensitivity scores and simple architectural rules to generate diverse, high-quality pruning configurations. The framework addresses a key limitation that different layers and architectures respond differently to pruning, making single-strategy approaches suboptimal. Mix-and-Match derives architecture-aware sparsity ranges, e.g., preserving normalization layers while pruning classifiers more aggressively, and systematically samples these ranges to produce ten strategies per sensitivity signal (magnitude, gradient, or their combination). This eliminates repeated pruning runs while offering deployment-ready accuracy-sparsity trade-offs. Experiments on CNNs and Vision Transformers demonstrate Pareto-optimal results, with Mix-and-Match reducing accuracy degradation on Swin-Tiny by 40% relative to standard single-criterion pruning. These findings show that coordinating existing pruning signals enables more reliable and efficient compressed models than introducing new criteria.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-10 (test) (Accuracy, Sparsity, and Drop)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-accuracy-sparsity-and-drop.md>): Image Classification; CIFAR-10 (test); Sparsity: 54.08.

- [Image Classification on CIFAR-10 VGG-11 (test)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-vgg-11-test.md>): Image Classification; CIFAR-10 VGG-11 (test); Sparsity: 90.4.

- [Image Classification on CIFAR-10 ResNet-18 (test)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-resnet-18-test.md>): Image Classification; CIFAR-10 Resnet-18 (test); Sparsity: 87.28.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.20280>)

[PDF](<https://arxiv.org/pdf/2603.20280.pdf>)