Paper Info
Title
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
Abstract
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 60x a software version on the ZYNQ 7020 SoC and still consume less than 0.43 W. 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.
Year
DOI
Venue
2021
10.3390/s21186050
SENSORS
Keywords
DocType
Volume
cloud computing, deep neural networks (DNNs), edge computing, field programmable gate array (FPGA), hardware acceleration, high-level synthesis (HLS) tools, internet of things (IoT), low-power, low-cost, Python framework
Journal
21
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Tarek Belabed100.34
Vitor Gaspar Silva27311.05
Alexandre Quenon300.34
Carlos Valderamma400.34
Chokri Souani5418.75