The deployment of Convolutional Neural Networks (CNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made large strides in developing network pruning methods for reducing the computing overhead of CNNs, there remains considerable accuracy loss, especially at high pruning ratios. Questioning that the architectures designed for non-pruned networks might not be effective for pruned networks, we propose to search architectures for pruning methods by defining a new search space and a novel search objective. To improve the generalization of the pruned networks, we propose two novel PrunedConv and PrunedLinear operations. Specifically, these operations mitigate the problem of unstable gradients by regularizing the objective function of the pruned networks. The proposed search objective enables us to train architecture parameters regarding the pruned weight elements. Quantitative analyses demonstrate that our searched architectures outperform those used in the state-of-the-art pruning networks on CIFAR-10 and ImageNet. In terms of hardware effectiveness, PR-DARTS increases MobileNet-v2's accuracy from 73.44% to 81.35% (+7.91% improvement) and runs 3.87$\times$ faster.

翻译：在边缘装置上部署革命神经网络(CNNs)的工作受到性能要求和现有处理能力之间巨大差距的阻碍。虽然最近的研究在开发网络运行方法以减少CNN计算间接成本方面取得了长足进步,但准确性损失仍然相当大,特别是在高纯度比率方面。质疑为非经处理网络设计的建筑对经处理的网络可能无效,我们提议通过定义新的搜索空间和新的搜索目标来搜索修剪方法的结构。为了改进经处理网络的普及化,我们提议了两本小说Pruned Convon和PrunedLinear 操作。具体地说,这些行动通过调整经处理网络的客观功能,缓解了不稳定的梯度问题。拟议的搜索目标使我们能够对经处理的重量元素进行建筑参数培训。定量分析表明,我们的搜索结构超过了在CIFAR-10和图像网络上用于最先进的剪裁网络所使用的结构。在硬件效率方面,PR-DART-91%和PrunedLined Linal 操作中提高了移动网络3.44%的精确度。