When building Deep Learning (DL) models, data scientists and software engineers manage the trade-off between their accuracy, or any other suitable success criteria, and their complexity. In an environment with high computational power, a common practice is making the models go deeper by designing more sophisticated architectures. However, in the context of mobile devices, which possess less computational power, keeping complexity under control is a must. In this paper, we study the performance of a system that integrates a DL model as a trade-off between the accuracy and the complexity. At the same time, we relate the complexity to the efficiency of the system. With this, we present a practical study that aims to explore the challenges met when optimizing the performance of DL models becomes a requirement. Concretely, we aim to identify: (i) the most concerning challenges when deploying DL-based software in mobile applications; and (ii) the path for optimizing the performance trade-off. We obtain results that verify many of the identified challenges in the related work such as the availability of frameworks and the software-data dependency. We provide a documentation of our experience when facing the identified challenges together with the discussion of possible solutions to them. Additionally, we implement a solution to the sustainability of the DL models when deployed in order to reduce the severity of other identified challenges. Moreover, we relate the performance trade-off to a new defined challenge featuring the impact of the complexity in the obtained accuracy. Finally, we discuss and motivate future work that aims to provide solutions to the more open challenges found.

翻译：当建立深层学习模型时,数据科学家和软件工程师管理其精确度或任何其他适当成功标准及其复杂性之间的权衡时,数据科学家和软件工程师在建立深层学习模型时管理其精确度或其他任何适当成功标准及其复杂性之间的权衡。在计算能力高的环境中,一种共同做法是使模型通过设计更复杂的结构而更深入。然而,在移动设备方面,拥有较少计算能力,控制复杂性是必要的。在本文件中,我们研究将DL模型作为精确度和复杂度之间的权衡的系统性能。与此同时,我们把复杂性与系统的效率联系起来。我们提出一项实际研究,目的是探讨在优化DL模型的性能成为一项要求时遇到的挑战。具体地说,我们的目标是查明:(一) 在移动应用程序中部署DL软件时遇到的挑战最多;和(二) 优化性能交换的路径。我们获得的结果可以核实相关工作中发现的许多挑战,例如框架的可用性和软件数据依赖性。我们用文件记录了我们在面对所查明的挑战时遇到的挑战,同时在优化D模型的性表现能力成为一项要求时,具体来说,我们在讨论所部署的预期的可持续性方面,我们为了实现其他目标,我们所确定的可持续性,我们把所要达到的目标联系起来。