An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract)

Abstract Deep learning (DL), despite its success in various fields, remains expensive and inaccessible to many due to its need for powerful supercomputing and high-end GPUs. This study explores alternative computing infrastructure and methods for distributed DL on low-energy, low-cost devices. We experiment on Raspberry Pi 4 devices with ARM Cortex-A72 processors and train a ResNet-18 model on the CIFAR-10 dataset. Our findings reveal limitations and opportunities for future optimizations, paving the way for a DL toolset for low-energy edge devices.