Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models
Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models
复制标题
DOI：
10.1145/3366636
复制
发表时间：
2020-02
期刊：
影响因子：
--
通讯作者：
Nitthilan Kanappan Jayakodi;Syrine Belakaria;Aryan Deshwal;J. Doppa
中科院分区：
文献类型：
--
作者：
Nitthilan Kanappan Jayakodi;Syrine Belakaria;Aryan Deshwal;J. Doppa
许多现实世界的边缘应用，包括对象检测、机器人和智能健康，都是通过在能源受限的移动平台上部署深度神经网络 (DNN) 来实现的。在本文中，我们提出了一种新颖的方法，使用称为学习能量精度权衡网络（LEANets）的设计空间来权衡运行时的能量和推理准确性。 LEANet 背后的关键思想是使用预训练的 DNN 来设计复杂性不断增加的分类器，以执行特定于输入的自适应推理。自适应推理方案的准确性和能耗取决于一组阈值，每个分类器都有一个阈值。为了确定阈值向量集以实现不同的能量和精度权衡，我们提出了一种新颖的多目标优化方法。我们可以根据所需的权衡在运行时选择适当的阈值向量。我们使用不同的图像分类数据集对多个预训练的 DNN 进行实验，包括 ConvNet、VGG-16 和 MobileNet。我们的结果表明，我们获得了高达 50% 的能量增益，而精度损失可以忽略不计，并且与称为 Slimmable 神经网络的最先进方法相比，优化的 LEANet 实现了明显更好的能量和精度权衡。
Many real-world edge applications including object detection, robotics, and smart health are enabled by deploying deep neural networks (DNNs) on energy-constrained mobile platforms. In this article, we propose a novel approach to trade off energy and accuracy of inference at runtime using a design space called Learning Energy Accuracy Tradeoff Networks (LEANets). The key idea behind LEANets is to design classifiers of increasing complexity using pretrained DNNs to perform input-specific adaptive inference. The accuracy and energy consumption of the adaptive inference scheme depends on a set of thresholds, one for each classifier. To determine the set of threshold vectors to achieve different energy and accuracy tradeoffs, we propose a novel multiobjective optimization approach. We can select the appropriate threshold vector at runtime based on the desired tradeoff. We perform experiments on multiple pretrained DNNs including ConvNet, VGG-16, and MobileNet using diverse image classification datasets. Our results show that we get up to a 50% gain in energy for negligible loss in accuracy, and optimized LEANets achieve significantly better energy and accuracy tradeoff when compared to a state-of-the-art method referred to as Slimmable neural networks.