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With rising powerful, low-cost embedded devices, the edge computing hasbecome an increasingly popular choice. In this paper, we propose a newincremental local stochastic gradient descent (SGD) tailored on the RaspberryPi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with1,000 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 eachdata partition to classify the data locally. The incremental local SGDsequentially loads small data blocks of the training dataset to learn local SGDmodels. The numerical test results on Imagenet dataset show that ourincremental local SGD algorithm with the Raspberry Pi 4 is faster and moreaccurate than the state-of-the-art linear SVM run on a PC Intel(R) Core i7-4790CPU, 3.6 GHz, 4 cores.

ImageNet Challenging Classification with the Raspberry Pi: An Incremental Local Stochastic Gradient Descent Algorithm