The ‘Internet of Things’ has brought increased demand for artificial intelligence-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 advances the feasibility of using highly compressed neural networks for energy-efficient neural computing in edge devices.

从医疗保健监控系统到自动驾驶汽车，“物联网”对基于人工智能的边缘计算的需求日益增长。量化是解决此类应用程序不断增长的计算成本的强大工具，并且可以在全精度网络上产生显着的压缩效果。但是，量化可能会导致复杂图像分类任务的性能大幅下降。为了解决这个问题，我们提出了一种由主成分分析（PCA）驱动的方法，以识别二进制网络的重要层，并设计混合精度网络。提议的Hybrid-Net相对于二进制网络（例如，针对ResNet的XNOR-Net和CIFAR-100和ImageNet数据集上的VGG架构）的分类准确性提高了10％以上，同时仍可达到XNOR-Nets高达94％的能源效率。这项工作提高了将高度压缩的神经网络用于边缘设备中的节能神经计算的可行性。