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DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
IEEE Transactions on Mobile Computing ( IF 7.9 ) Pub Date : 2020-02-01 , DOI: 10.1109/tmc.2019.2893250
Mengwei Xu , Feng Qian , Mengze Zhu , Feifan Huang , Saumay Pushp , Xuanzhe Liu

Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X execution speedup, as well as 53.5 and 85.5 percent energy saving compared to wearable-only and handheld-only strategies, respectively.

中文翻译:

DeepWear:用于可穿戴深度学习的自适应局部卸载

由于其贴身和无处不在的特性,可穿戴设备可以生成范围广泛的独特传感器数据,为深度学习任务创造无数机会。我们提出了 DeepWear,这是一种用于可穿戴设备的深度学习 (DL) 框架,以提高性能并减少能源足迹。DeepWear 通过本地网络连接(例如蓝牙)将 DL 任务从可穿戴设备战略性地卸载到其配对的手持设备。与基于远程云的卸载相比,DeepWear 不需要互联网连接,消耗更少的能源,并且对隐私泄露具有强大的抵抗力。DeepWear 提供了各种新技术,例如上下文感知卸载、战略模型分区和流水线支持,以有效利用附近配对手持设备的处理能力。部署为用户空间库,DeepWear 提供了对开发人员友好的 API,这些 API 与 TensorFlow 等传统 DL 库中的 API 一样简单。我们已经在 Android 操作系统上实现了 DeepWear,并在具有真实深度学习模型的 COTS 智能手机和智能手表上对其进行了评估。与仅可穿戴和仅手持策略相比,DeepWear 分别带来高达 5.08 倍和 23.0 倍的执行加速,以及 53.5% 和 85.5% 的节能。
更新日期:2020-02-01
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