The energy consumption of Convolutional Neural Networks (CNNs) is a critical factor in deploying deep learning models on resource-limited equipment such as mobile devices and autonomous vehicles. We propose an approach involving Proportional Layer Skipping (PLS) and Frequency Scaling (FS). Layer skipping reduces computational complexity by selectively bypassing network layers, whereas frequency scaling adjusts the frequency of the processor to optimize energy use under latency constraints. Experiments of PLS and FS on ResNet-152 with the CIFAR-10 dataset demonstrated significant reductions in computational demands and energy consumption with minimal accuracy loss. This study offers practical solutions for improving real-time processing in resource-limited settings and provides insights into balancing computational efficiency and model performance.

翻译：卷积神经网络（CNN）的能耗是在移动设备和自动驾驶汽车等资源受限设备上部署深度学习模型的关键因素。我们提出了一种结合比例层跳过（PLS）与频率缩放（FS）的方法。层跳过通过选择性绕过网络层来降低计算复杂度，而频率缩放则在延迟约束下调整处理器频率以优化能耗。在ResNet-152模型与CIFAR-10数据集上进行的PLS与FS实验表明，该方法能以极小的精度损失显著降低计算需求和能耗。本研究为提升资源受限环境下的实时处理能力提供了实用解决方案，并对平衡计算效率与模型性能提供了见解。