论文标题
天鹅：用于有效DNN智能手机SOC培训的神经引擎
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
论文作者
论文摘要
需要在最终用户设备（例如智能手机）上训练DNN模型的需求，而随着需要提高数据隐私并减少通信开销的需求。与具有强大CPU和GPU的数据中心服务器不同，现代智能手机由多种专门核心组成，遵循系统上的芯片（SOC）体系结构，共同执行各种任务。我们观察到，在智能手机SOC上进行培训DNN而不仔细考虑其资源限制不仅会导致次优培训表现，而且还会显着影响用户体验。在本文中，我们介绍了天鹅，这是一种神经引擎，可在不损害用户体验的情况下优化智能手机SOC的DNN培训。广泛的大规模评估表明，天鹅可以比最先进的表现提高1.2-23.3倍。
The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter servers with powerful CPUs and GPUs, modern smartphones consist of a diverse collection of specialized cores following a system-on-a-chip (SoC) architecture that together perform a variety of tasks. We observe that training DNNs on a smartphone SoC without carefully considering its resource constraints can not only lead to suboptimal training performance but significantly affect user experience as well. In this paper, we present Swan, a neural engine to optimize DNN training on smartphone SoCs without hurting user experience. Extensive large-scale evaluations show that Swan can improve performance by 1.2 - 23.3x over the state-of-the-art.
