English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning