Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment

2026-05-21 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Slimmable ConvNeXt is a novel approach for width-adaptive inference, enabling efficient deployment of vision models across devices with varying resource constraints using a single set of shared weights. This method addresses the need to train and maintain separate models for different compute capacities by integrating multiple nested subnetworks. The design leverages ConvNeXt's modern architecture, specifically LayerNorm and inverted bottlenecks, to simplify the training pipeline and eliminate the normalization overhead common in prior slimmable CNNs. On ImageNet-1k, Slimmable ConvNeXt-T with 3 subnetworks achieved 80.8% top-1 accuracy at 4.5 GMACs and 77.4% at 1.2 GMACs after 600 epochs. This performance surpasses HydraViT's 6-head subnetwork by 2.4 percentage points and its 3-head configuration by 4.4 percentage points at comparable compute, also outperforming MatFormer-S (78.6%) and SortedNet-S (78.2%). Scaling to Slimmable ConvNeXt-B further improved maximum accuracy to 82.8% at 15.35 GMACs.

Key takeaway

For Machine Learning Engineers deploying vision models across varied hardware, Slimmable ConvNeXt offers a streamlined solution. You can achieve efficient multi-device deployment with a single model, eliminating the need to train and maintain separate versions for different resource constraints. This approach simplifies your pipeline and improves performance, allowing your applications to dynamically adapt to available compute without significant overhead. Consider integrating this architecture to optimize resource utilization and reduce operational complexity.

Key insights

Slimmable ConvNeXt enables efficient width-adaptive inference for vision models using a single, shared-weight architecture.

Principles

• Modern CNN designs simplify width-adaptive training.
• LayerNorm and inverted bottlenecks aid channel slimming.
• Shared weights support multi-capacity subnetworks.

Method

Slimmable ConvNeXt trains a single set of shared weights containing multiple nested subnetworks. It leverages ConvNeXt's LayerNorm and inverted bottlenecks to simplify the training pipeline.

In practice

• Deploy vision models across diverse devices.
• Adapt model capacity to fluctuating compute.
• Reduce model maintenance overhead.

Topics

• Slimmable ConvNeXt
• Width-Adaptive Inference
• Multi-Device Deployment
• Vision Models
• ImageNet-1k
• Efficient Inference

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.