Block Pruning for Enhanced Efficiency in Convolutional Neural Networks
This work addresses model efficiency for edge computing by providing an incremental improvement in pruning techniques.
The paper tackled network pruning for convolutional neural networks by introducing a direct block removal strategy to assess block importance, achieving reduced model size while retaining high accuracy on datasets like ImageNet with ResNet50, even with significant pruning.
This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments. Our method diverges from traditional techniques that utilize proxy metrics, instead employing a direct block removal strategy to assess the impact on classification accuracy. This hands-on approach allows for an accurate evaluation of each block's importance. We conducted extensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using ResNet architectures. Our results demonstrate the efficacy of our method, particularly on large-scale datasets like ImageNet with ResNet50, where it excelled in reducing model size while retaining high accuracy, even when pruning a significant portion of the network. The findings underscore our method's capability in maintaining an optimal balance between model size and performance, especially in resource-constrained edge computing scenarios.