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Proceedings of the Workshop on Benchmarking Cyber-Physical Systems and Internet of Things
Advances in deep learning have revolutionized machine learning by solving complex tasks such as image, speech, and text recognition. However, training and inference of deep neural networks are resource-intensive. Recently, researchers made efforts to bring inference to IoT edge and sensor devices which have become the prime data sources nowadays. However, running deep neural networks on low-power IoT devices is challenging due to their resource-constraints in memory, compute power, and energy.doi:10.1145/3458473.3458823 fatcat:yacip4x67ffwxg2lywnj5iq2qi