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

Designing Deep Neural Networks (DNNs) running on edge hardware remains achallenge. Standard designs have been adopted by the community to facilitatethe deployment of Neural Network models. However, not much emphasis is put onadapting the network topology to fit hardware constraints. In this paper, weadapt one of the most widely used architectures for mobile hardware platforms,MobileNetV2, and study the impact of changing its topology and applyingpost-training quantization. We discuss the impact of the adaptations and thedeployment of the model on an embedded hardware platform for face detection.

Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform