论文标题
对内存约束设备的图像分类技术的定量分析
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices
论文作者
论文摘要
卷积神经网络或CNN是图像分类的最新技术，但通常以大型内存足迹为代价。这限制了它们在依赖嵌入式设备的应用程序中的实用性，其中内存通常是一个稀缺的资源。最近，在这种内存受限的设备上的图像分类领域取得了重大进展，并具有诸如Protonn，Bonsai和FastGrnn算法之类的新贡献。使用MNIST-10，它们的光学特征识别可达到98.2％的精度，而内存足迹仅为6KB。但是，它们在更复杂的多类和多通道图像分类上的潜力尚未确定。在本文中，当使用CIFAR-10应用于3通道图像分类时，我们将CNN与Protonn，Bonsai和FastGrnn进行比较。为了进行分析，我们使用现有的直接卷积算法来实现CNNS内存，并提出了调整FastGrnn模型以使用多通道图像的新方法。我们将每种算法的评估扩展到8KB，16KB，32KB，64KB和128KB的记忆尺寸预算，以表明直接卷积CNN的表现最佳，最高速度为65.7％，在58.23kb的内存足迹下，最高率为65.7％。
Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness in applications relying on embedded devices, where memory is often a scarce resource. Recently, there has been significant progress in the field of image classification on such memory-constrained devices, with novel contributions like the ProtoNN, Bonsai and FastGRNN algorithms. These have been shown to reach up to 98.2% accuracy on optical character recognition using MNIST-10, with a memory footprint as little as 6KB. However, their potential on more complex multi-class and multi-channel image classification has yet to be determined. In this paper, we compare CNNs with ProtoNN, Bonsai and FastGRNN when applied to 3-channel image classification using CIFAR-10. For our analysis, we use the existing Direct Convolution algorithm to implement the CNNs memory-optimally and propose new methods of adjusting the FastGRNN model to work with multi-channel images. We extend the evaluation of each algorithm to a memory size budget of 8KB, 16KB, 32KB, 64KB and 128KB to show quantitatively that Direct Convolution CNNs perform best for all chosen budgets, with a top performance of 65.7% accuracy at a memory footprint of 58.23KB.
