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

Deep learning using convolutional neural networks (CNNs) is quickly becomingthe state-of-the-art for challenging computer vision applications. However,deep learning's power consumption and bandwidth requirements currently limitits application in embedded and mobile systems with tight energy budgets. Inthis paper, we explore the energy savings of optically computing the firstlayer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs),custom CMOS diffractive image sensors which act similar to Gabor filter banksin the V1 layer of the human visual cortex. ASPs replace both image sensing andthe first layer of a conventional CNN by directly performing optical edgefiltering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Ourexperimental results (both on synthetic data and a hardware prototype) for avariety of vision tasks such as digit recognition, object recognition, and faceidentification demonstrate using ASPs while achieving similar performancecompared to traditional deep learning pipelines.

ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels