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

In recent years, advances in deep learning have resulted in unprecedentedleaps in diverse tasks spanning from speech and object recognition to contextawareness and health monitoring. As a result, an increasing number ofAI-enabled applications are being developed targeting ubiquitous and mobiledevices. While deep neural networks (DNNs) are getting bigger and more complex,they also impose a heavy computational and energy burden on the host devices,which has led to the integration of various specialized processors in commoditydevices. Given the broad range of competing DNN architectures and theheterogeneity of the target hardware, there is an emerging need to understandthe compatibility between DNN-platform pairs and the expected performancebenefits on each platform. This work attempts to demystify this landscape bysystematically evaluating a collection of state-of-the-art DNNs on a widevariety of commodity devices. In this respect, we identify potentialbottlenecks in each architecture and provide important guidelines that canassist the community in the co-design of more efficient DNNs and accelerators.

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices