Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures

TL;DR

This paper presents a novel approach to making CNN architectures dynamically adjustable at runtime through structured pruning and re-construction, enabling efficient deployment on resource-limited devices.

Contribution

It introduces a structured prune-and-grow method for CNNs that allows dynamic capacity adjustment without retraining, enhancing flexibility and robustness.

Findings

Adaptive CNNs maintain or improve accuracy under resource constraints.

The method works across multiple architectures and datasets.

Dynamic models outperform static counterparts in resource-limited scenarios.

Abstract

Deploying deep convolutional neural networks (CNNs) on resource-constrained devices presents significant challenges due to their high computational demands and rigid, static architectures. To overcome these limitations, this thesis explores methods for enabling CNNs to dynamically adjust their computational complexity based on available hardware resources. We introduce adaptive CNN architectures capable of scaling their capacity at runtime, thus efficiently balancing performance and resource utilization. To achieve this adaptability, we propose a structured pruning and dynamic re-construction approach that creates nested subnetworks within a single CNN model. This approach allows the network to dynamically switch between compact and full-sized configurations without retraining, making it suitable for deployment across varying hardware platforms. Experiments conducted across multiple CNN architectures including VGG-16, AlexNet, ResNet-20, and ResNet-56 on CIFAR-10 and Imagenette datasets demonstrate that adaptive models effectively maintain or even enhance performance under varying computational constraints. Our results highlight that embedding adaptability directly into CNN architectures significantly improves their robustness and flexibility, paving the way for efficient real-world deployment in diverse computational environments.