The success of Deep Neural Networks (DNNs) is attributed to three factors: increased computing capacity, more complex models, and more data. These factors, however, are not always available especially for edge applications such as autonomous driving, augmented reality, and internet-of-things.
Training DNNs requires a large amount of data, which are difficult to obtain. Edge devices such as mobile phones have limited computing capacity, therefore, require specialized and efficient DDNs. However, due to the enormous design space and prohibitive training coasts, designing efficient DDNs for different target devices is challenging. So the question is, with limited data, computing capacity, and model complexity, can we still succesfully apply Deep Neural Networks?
