论文标题
便宜3：远程DNN型号的成本效益
CheapET-3: Cost-Efficient Use of Remote DNN Models
论文作者
论文摘要
关于复杂的问题，可以使用非常大规模的模型来实现最深神网络（DNN）的最佳预测准确性，包括数十亿个参数。这样的模型只能在专用服务器上运行，该服务器通常由第三方服务提供，这会导致每个预测的巨额货币成本。我们为客户端应用程序提出了一种新的软件体系结构，该软件架构与远程大规模模型一起使用，旨在以可忽略的货币成本在本地进行简单的预测，同时仍利用大型模型的好处来获得具有挑战性的投入。在概念证明中，我们将预测成本降低了50％，而不会对系统的准确性产生负面影响。
On complex problems, state of the art prediction accuracy of Deep Neural Networks (DNN) can be achieved using very large-scale models, consisting of billions of parameters. Such models can only be run on dedicated servers, typically provided by a 3rd party service, which leads to a substantial monetary cost for every prediction. We propose a new software architecture for client-side applications, where a small local DNN is used alongside a remote large-scale model, aiming to make easy predictions locally at negligible monetary cost, while still leveraging the benefits of a large model for challenging inputs. In a proof of concept we reduce prediction cost by up to 50% without negatively impacting system accuracy.
