论文标题
Imagenet具有覆盆子PI的挑战性分类：一种局部局部随机梯度下降算法
ImageNet Challenging Classification with the Raspberry Pi: An Incremental Local Stochastic Gradient Descent Algorithm
论文作者
论文摘要
随着功能强大，低成本嵌入式设备的上升，边缘计算已成为越来越受欢迎的选择。在本文中，我们提出了一个在Raspberry Pi上量身定制的新的局部随机梯度下降（SGD），以处理具有1,000个类别的1,261,405张图像的大型Imagenet ILSVRC 2010数据集。本地SGD使用$ k $表示算法将数据块分为$ k $分区，然后以每个数据分区中的平行方式学习SGD模型，以在本地对数据进行分类。增量本地SGD顺序加载训练数据集的小数据块，以学习本地SGD模型。 ImageNet数据集上的数值测试结果表明，与Raspberry Pi 4相比，我们的局部局部SGD算法比在PC Intel（R）Intel（R）核心I7-4790 CPU上运行的最新线性SVM更快，更准确。
With rising powerful, low-cost embedded devices, the edge computing has become an increasingly popular choice. In this paper, we propose a new incremental local stochastic gradient descent (SGD) tailored on the Raspberry Pi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with 1,000 classes. The local SGD splits the data block into $k$ partitions using $k$means algorithm and then it learns in the parallel way SGD models in each data partition to classify the data locally. The incremental local SGD sequentially loads small data blocks of the training dataset to learn local SGD models. The numerical test results on Imagenet dataset show that our incremental local SGD algorithm with the Raspberry Pi 4 is faster and more accurate than the state-of-the-art linear SVM run on a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores.
