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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware and software. In this paper, a novel design framework fully automated for Edge applications is proposed to perform such a deployment on System-on-Chips. Based on a high-level Python interface that mimics the leading Deep Learning software frameworks, it offers an easy way to implement a hardware-accelerated DNN on an FPGA. To do this, our design methodology covers the three main phases: (a) customization: where the user specifies the optimizations needed on each DNN layer, (b) generation: the framework generates on the Cloud the necessary binaries for both FPGA and software parts, and (c) deployment: the SoC on the Edge receives the resulting files serving to program the FPGA and related Python libraries for user applications. Among the study cases, an optimized DNN for the MNIST database can speed up more than 60× a software version on the ZYNQ 7020 SoC and still consume less than 0.43W. A comparison with the state-of-the-art frameworks demonstrates that our methodology offers the best trade-off between throughput, power consumption, and system cost.

sensors

A Novel Automate Python Edge-to-Edge: From Automated Generation on Cloud to User Application Deployment on Edge of Deep Neural Networks for Low Power IoT Systems FPGA-Based Acceleration