Abstract
The recent advent of IOT has increased the demand for enabling AI-based edge computing in several applications including healthcare monitoring systems, autonomous vehicles etc. This has necessitated the search for efficient implementations of neural networks in terms of both computation and storage. Although extreme quantization has proven to be a powerful tool to achieve significant compression over full-precision networks, it can result in significant degradation in performance for complex image classification tasks. In this work, we propose a Principal Component Analysis (PCA) driven methodology to design mixed-precision, hybrid networks. Unlike standard practices of using PCA for dimensionality reduction, we leverage PCA to identify significant layers in a binary network which contribute relevant transformations on the input data by increasing the number of significant dimensions. Subsequently, we propose Hybrid-Net, a network with increased bit-precision of the weights and activations of the significant layers in a binary network. We show that the proposed Hybrid-Net achieves over 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 upto 94% of the energy-efficiency of XNOR-Nets. The proposed design methodology allows us to move closer to the accuracy of standard full-precision networks by keeping more than half of the network binary. This work demonstrates an effective, one-shot methodology for designing hybrid, mixed-precision networks which significantly improve the classification performance of binary networks while attaining remarkable compression. The proposed hybrid networks further the feasibility of using highly compressed neural networks for energy-efficient neural computing in IOT-based edge devices.
Abstract (translated)
最近，物联网的出现增加了在医疗保健监控系统、自动车辆等多个应用中启用基于人工智能的边缘计算的需求。这就需要在计算和存储方面搜索神经网络的有效实现。尽管极端量化已被证明是在全精度网络上实现显著压缩的强大工具，但它可能导致复杂图像分类任务的性能显著下降。在这项工作中，我们提出一个主成分分析（PCA）驱动的方法来设计混合精度，混合网络。与使用PCA进行维数约简的标准实践不同，我们利用PCA来识别二进制网络中的重要层，通过增加重要维数，这些层有助于输入数据的相关转换。然后，我们提出了混合网络，一种二进制网络中有效层的权值和激活位精度提高的网络。我们表明，在cifar-100和imagenet数据集的resnet和vgg架构中，所提出的混合网络比二进制网络（如xnor net）的分类精度提高了10%以上，同时仍然达到xnor网络能效的94%。所提出的设计方法使我们能够通过保持超过一半的网络二进制来接近标准全精度网络的精度。这项工作证明了一种有效的、一次性的方法来设计混合的、混合精度的网络，在获得显著压缩的同时显著提高了二进制网络的分类性能。所提出的混合网络进一步证明了在基于物联网的边缘设备中使用高压缩神经网络进行高效神经计算的可行性。
URL
https://arxiv.org/abs/1906.01493
