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On complex problems, state of the art prediction accuracy of Deep NeuralNetworks (DNN) can be achieved using very large-scale models, consisting ofbillions of parameters. Such models can only be run on dedicated servers,typically provided by a 3rd party service, which leads to a substantialmonetary cost for every prediction. We propose a new software architecture forclient-side applications, where a small local DNN is used alongside a remotelarge-scale model, aiming to make easy predictions locally at negligiblemonetary cost, while still leveraging the benefits of a large model forchallenging inputs. In a proof of concept we reduce prediction cost by up to50% without negatively impacting system accuracy.

CheapET-3: Cost-Efficient Use of Remote DNN Models