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

Cascade systems comprise a two-model sequence, with a lightweight modelprocessing all samples and a heavier, higher-accuracy model conditionallyrefining harder samples to improve accuracy. By placing the light model on thedevice side and the heavy model on a server, model cascades constitute a widelyused distributed inference approach. With the rapid expansion of intelligentindoor environments, such as smart homes, the new setting of Multi-DeviceCascade is emerging where multiple and diverse devices are to simultaneouslyuse a shared heavy model on the same server, typically located within or closeto the consumer environment. This work presents MultiTASC, amulti-tenancy-aware scheduler that adaptively controls the forwarding decisionfunctions of the devices in order to maximize the system throughput, whilesustaining high accuracy and low latency. By explicitly considering deviceheterogeneity, our scheduler improves the latency service-level objective (SLO)satisfaction rate by 20-25 percentage points (pp) over state-of-the-art cascademethods in highly heterogeneous setups, while serving over 40 devices,showcasing its scalability.

MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge