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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Photonic computing promises faster and more energy-efficient deep neuralnetwork (DNN) inference than traditional digital hardware. Advances in photoniccomputing can have profound impacts on applications such as autonomous drivingand defect detection that depend on fast, accurate and energy efficientexecution of image segmentation models. In this paper, we investigate imagesegmentation on photonic accelerators to explore: a) the types of imagesegmentation DNN architectures that are best suited for photonic accelerators,and b) the throughput and energy efficiency of executing the different imagesegmentation models on photonic accelerators, along with the trade-offsinvolved therein. Specifically, we demonstrate that certain segmentation modelsexhibit negligible loss in accuracy (compared to digital float32 models) whenexecuted on photonic accelerators, and explore the empirical reasoning fortheir robustness. We also discuss techniques for recovering accuracy in thecase of models that do not perform well. Further, we compare throughput(inferences-per-second) and energy consumption estimates for different imagesegmentation workloads on photonic accelerators. We discuss the challenges andpotential optimizations that can help improve the application of photonicaccelerators to such computer vision tasks.

Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection