The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles. Quantization is a powerful tool to address the growing computational cost of such applications, and yields significant compression over full-precision networks. However, quantization can result in substantial loss of performance for complex image classification tasks. To address this, we propose a Principal Component Analysis (PCA) driven methodology to identify the important layers of a binary network, and design mixed-precision networks. The proposed Hybrid-Net achieves a more than 10% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving up to 94% of the energy-efficiency of XNOR-Nets. This work furthers the feasibility of using highly compressed neural networks for energy-efficient neural computing in edge devices.
翻译:“物联网”在从保健监测系统到自主车辆等各种应用中增加了对基于AI的边缘计算的需求。量化是解决此类应用的计算成本不断增加的有力工具,对全精度网络造成大量压缩。然而,量化可能导致复杂图像分类任务的性能大幅下降。为解决这一问题,我们提议了一项主要组成部分分析驱动方法,以确定二进制网络的重要层面,并设计混合精度网络。拟议的混合网络比二进制网络,如CIFAR-100的ResNet XNOR-Net和VGG结构以及图像网络数据集等二进制网络的分类精确度提高了10%以上,同时仍然达到XNOR-Net的能源效率的94%。这项工作进一步推进了在边缘装置中使用高度压缩神经网络进行节能神经计算的可行性。