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.

该论文介绍了一种新的网络剪枝方法，针对边缘计算环境中的深度神经网络中的块剪枝。研究方法采用直接的块移除策略来评估对分类准确性的影响，通过对 CIFAR-10、CIFAR-100 和 ImageNet 数据集以 ResNet 架构进行广泛实验，结果表明该方法在减小模型大小的同时保持高准确性方面的有效性，在网络的大部分被剪枝的情况下，尤其是在 ImageNet 上通过对 ResNet50 的剪枝时，其结果突出了该方法在维持模型大小和性能之间的最佳平衡能力，尤其是在资源有限的边缘计算场景中。