Supervised Hardware/Software Partitioning Algorithms for FPGA-based Applications

Belhedi Wiem, Hannachi Marwa

2020

Abstract

Real time systems require the cooperation of the reconfigurable hardware and the software in order to boost the application performance in terms of both energy and time. However, the integration of these systems presents a hardware/software co-design challenges in terms of both time minimization and autonomy; hence, the importance of hardware/software partitioning algorithms. Here, we present a selection of artificial intelligence based-approaches that we apply in order to solve the hardware/software classification task in real-time systems. For this, the used database consists of a collection of real experiments that were conducted in Altran Technologies. The tested classification algorithms include Linear Regression model optimized with gradient descent, logistic regression, Support vector machine (SVM), Linear Discriminant Analysis (LDA), and deep neural network (DNN). Results show the applicability of these methods and the high accuracy of the task type decision.

Paper Citation

in Harvard Style

Wiem B. and Marwa H. (2020). Supervised Hardware/Software Partitioning Algorithms for FPGA-based Applications. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 860-864. DOI: 10.5220/0009149708600864

in Bibtex Style

@conference{icaart20,
author={Belhedi Wiem and Hannachi Marwa},
title={Supervised Hardware/Software Partitioning Algorithms for FPGA-based Applications},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={860-864},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009149708600864},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Supervised Hardware/Software Partitioning Algorithms for FPGA-based Applications
SN - 978-989-758-395-7
AU - Wiem B.
AU - Marwa H.
PY - 2020
SP - 860
EP - 864
DO - 10.5220/0009149708600864