Authors:
Belhedi Wiem
and
Hannachi Marwa
Affiliation:
Department of Research, Altran Technologies, France
Keyword(s):
Hardware/Software Partitioning, Linear Regression, Logistic Regression, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Deep Neural Network (DNN).
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.