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.

翻译：本文提出一种面向深度神经网络块剪枝的新方法，针对边缘计算环境下的模型压缩需求。该方法摒弃了传统基于代理度量的技术路线，转而采用直接移除网络块的策略来评估其对分类精度的影响。这种直接实践方式能够准确衡量每个网络块的重要性。我们在CIFAR-10、CIFAR-100和ImageNet数据集上基于ResNet架构进行了系统实验。实验结果表明，该方法在大规模数据集（如ImageNet + ResNet50）上表现尤为突出，即使在剪除网络显著比例的情况下，仍能有效减小模型体积并保持高精度。研究结论证实了该方法在模型规模与性能之间维持最优平衡的能力，特别适用于资源受限的边缘计算场景。