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 convolutional neural networks (CNN) are widely used in modern artificialintelligence (AI) and smart vision systems but also limited by computationlatency, throughput, and energy efficiency on a resource-limited scenario, suchas mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV),and so on. A hardware streaming architecture is proposed to accelerateconvolution and pooling computations for state-of-the-art deep CNNs. It isoptimized for energy efficiency by maximizing local data reuse to reduceoff-chip DRAM data access. In addition, image and feature decompositiontechniques are introduced to optimize memory access pattern for an arbitrarysize of image and number of features within limited on-chip SRAM capacity. Aprototype accelerator was implemented in TSMC 65 nm CMOS technology with 2.3 mmx 0.8 mm core area, which achieves 144 GOPS peak throughput and 0.8 TOPS/W peakenergy efficiency.

A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications