DeepWear：用于可穿戴深度学习的自适应局部卸载
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
摘要：
由于可穿戴设备的身体和无处不在的性质，它们可以生成各种独特的传感器数据，为深度学习任务创造无数机会。我们提出了DeepWear，一个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鲁棒性。DeepWear提供了各种新颖的技术，如上下文感知卸载，战略模型分区和流水线支持，以有效地利用附近配对手持设备的处理能力。DeepWear作为用户空间库部署，提供开发人员友好的API，与传统DL库（如TensorFlow）中的API一样简单。我们已经在Android操作系统上实现了DeepWear，并在COTS智能手机和具有真实DL模型的智能手表上进行了评估。DeepWear带来了高达5.08倍和23.0倍的执行加速，以及53.5%和85.5%的节能相比，仅可穿戴和仅手持策略，分别。
英文摘要：
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. 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% energy saving compared to wearable-only and handheld-only strategies, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
