论文标题
Orpheus：一个新的深度学习框架，可轻松部署和评估边缘推理
Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference
论文作者
论文摘要
优化跨边缘设备和优化目标（例如推理时间，内存足迹和功耗）的深度学习推断是由于神经网络无处不在的关键挑战。如今，生产深度学习框架提供了有用的抽象来帮助机器学习工程师和系统研究人员。但是，作为交换，他们可能会遇到兼容性挑战（尤其是在受限平台上），无法访问的代码复杂性或设计选择，从系统角度来看，这些选择却限制了研究。本文介绍了Orpheus，这是一个新的深度学习框架，可轻松制作，部署和评估推理优化。 Orpheus具有一个小的代码库，最小的依赖性以及集成其他第三方系统的简单过程。我们提出了一些初步评估结果。
Optimising deep learning inference across edge devices and optimisation targets such as inference time, memory footprint and power consumption is a key challenge due to the ubiquity of neural networks. Today, production deep learning frameworks provide useful abstractions to aid machine learning engineers and systems researchers. However, in exchange they can suffer from compatibility challenges (especially on constrained platforms), inaccessible code complexity, or design choices that otherwise limit research from a systems perspective. This paper presents Orpheus, a new deep learning framework for easy prototyping, deployment and evaluation of inference optimisations. Orpheus features a small codebase, minimal dependencies, and a simple process for integrating other third party systems. We present some preliminary evaluation results.
