  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.
