论文标题
EDCOMPRESS：数据流的能源感知模型压缩
EDCompress: Energy-Aware Model Compression for Dataflows
论文作者
论文摘要
边缘设备需要低能消耗，成本和小型尺寸。为了在边缘设备上有效部署卷积神经网络（CNN）模型，能量感知模型压缩变得极为重要。但是，现有工作并不能很好地研究这个问题，因为缺乏考虑硬件体系结构中数据流类型的多样性。在本文中，我们提出了EDCompress，这是一种用于各种数据流的能源感知模型压缩方法。它可以有效地减少具有不同数据流类型的各种边缘设备的能耗。考虑到模型压缩程序的本质，我们将优化过程重新塑造为多步问题，并通过增强学习算法解决。实验表明，EDCOMPRESS可以分别提高VGG-16，Mobilenet，Lenet-5网络的20倍，17倍，37倍的能效，而准确性损失可忽略不计。 EDCOMPRESS还可以在能源消耗方面找到特定神经网络的最佳数据流类型，这可以指导硬件系统上CNN模型的部署。
Edge devices demand low energy consumption, cost and small form factor. To efficiently deploy convolutional neural network (CNN) models on edge device, energy-aware model compression becomes extremely important. However, existing work did not study this problem well because the lack of considering the diversity of dataflow types in hardware architectures. In this paper, we propose EDCompress, an Energy-aware model compression method for various Dataflows. It can effectively reduce the energy consumption of various edge devices, with different dataflow types. Considering the very nature of model compression procedures, we recast the optimization process to a multi-step problem, and solve it by reinforcement learning algorithms. Experiments show that EDCompress could improve 20X, 17X, 37X energy efficiency in VGG-16, MobileNet, LeNet-5 networks, respectively, with negligible loss of accuracy. EDCompress could also find the optimal dataflow type for specific neural networks in terms of energy consumption, which can guide the deployment of CNN models on hardware systems.
