Deep convolutional neural networks (CNNs) are indispensable to
state-of-the-art computer vision algorithms. However, they are still rarely
deployed on battery-powered mobile devices, such as smartphones and wearable
gadgets, where vision algorithms can enable many revolutionary real-world
applications. The key limiting factor is the high energy consumption of CNN
processing due to its high computational complexity. While there are many
previous efforts that try to reduce the CNN model size or amount of
computation, we find that they do not necessarily result in lower energy
consumption, and therefore do not serve as a good metric for energy cost
estimation.
To close the gap between CNN design and energy consumption optimization, we
propose an energy-aware pruning algorithm for CNNs that directly uses energy
consumption estimation of a CNN to guide the pruning process. The energy
estimation methodology uses parameters extrapolated from actual hardware
measurements that target realistic battery-powered system setups. The proposed
layer-by-layer pruning algorithm also prunes more aggressively than previously
proposed pruning methods by minimizing the error in output feature maps instead
of filter weights. For each layer, the weights are first pruned and then
locally fine-tuned with a closed-form least-square solution to quickly restore
the accuracy. After all layers are pruned, the entire network is further
globally fine-tuned using back-propagation. With the proposed pruning method,
the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x,
respectively, with less than 1% top-5 accuracy loss. Finally, we show that
pruning the AlexNet with a reduced number of target classes can greatly
decrease the number of weights but the energy reduction is limited.
Energy modeling tool and energy-aware pruned models available at
this http URL

本研究提出了一种新的基于能量消耗估计的卷积神经网络 (CNNs) 剪枝算法，该算法比以前的算法更加激进，并具有更高的准确性，AlexNet 和 GoogLeNet 的能量消耗分别降低了 3.7x 和 1.6x，用于降低卷积神经网络在移动设备上的能量消耗的新工具和模型都可在链接网站上找到。