Abdessalem Ben Abdelali | University of Monastir (original) (raw)

Papers by Abdessalem Ben Abdelali

Research paper thumbnail of Node Localization in Range-Free 3D-WSNs Using New DV-Hop Algorithm Based Machine Learning Techniques

Research paper thumbnail of Cascade Machines Learning Process for Node Localization in Large-Scale Wireless Sensor Networks

Ingénierie Des Systèmes D'information, Aug 31, 2022

Localization is a crucial concern in many Wireless Sensor Network (WSN) applications. Moreover, g... more Localization is a crucial concern in many Wireless Sensor Network (WSN) applications. Moreover, getting accurate information about geographic positions of nodes (sensors) is very interesting to make the collected data useful and meaningful. The based connectivity algorithms aim to localize multi-hop WSN thanks to their advantages such as their simplicity and acceptable accuracy. However, the localization accuracy may be relatively low due to environment conditions. An Extreme Learning Machine technique (ELM) is given in this manuscript to minimize the localization error in Range-Free WSN. In this work, based on the Cascade-ELM, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to improve the localization accuracy in Range-Free WSN. We applied the proposed methods in different scenarios of Multi-hop WSN. In our study, we focused on an isotropic and irregular environment. Simulation results prove that the proposed Cascade-ELM algorithm greatly optimizes the localization accuracy in comparison with other algorithms issued from smart computing techniques. Improved localization performances, when compared to previous works, are obtained for isotropic environments.

Research paper thumbnail of Optimized Piccolo Lightweight Block Cipher: Area Efficient Implementation

Traitement Du Signal, Jun 30, 2022

Piccolo algorithm is one of the lightweight block ciphers designed specifically for lowresource d... more Piccolo algorithm is one of the lightweight block ciphers designed specifically for lowresource devices which present physical constraints in terms of area, power, and memory. Various hardware architectures for Piccolo block cipher have been proposed in recent years with the aim of obtaining a more appropriate low-resource design for specific constrained applications. The latter must meet real-time processing constraints without affecting the need for hardware resources. Finding a good compromise between computation time and implementation resource consumption is a major consideration in the design process. In this paper, we suggest six serial hardware architectures for Piccolo lightweight algorithm with a 128 bits key length. Proposed architectures are compared to existing designs based on hardware resource occupancy, latency, and throughput. Also, we tested the security of the Piccolo algorithm, and the obtained results show the good robustness of the Piccolo block cipher against statistical attacks. Thus, we can use the Piccolo algorithm in lightweight applications that require a high level of privacy.

Research paper thumbnail of New Online DV-Hop Algorithm via Mobile Anchor for Wireless Sensor Network Localization

Tsinghua Science & Technology, Oct 1, 2023

In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of u... more In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of users. Moreover, determining where and when that event occurs is crucial; thus, the positions of nodes must be identified. Subsequently, in a range-free case, the Distance Vector-Hop (DV-Hop) heuristic is the commonly used localization algorithm because of its simplicity and low cost. The DV-Hop algorithm consists of a set of reference nodes, namely, anchors, to periodically broadcast their current positions and assist nearby unknown nodes during localization. Another potential solution includes the use of only one mobile anchor instead of these sets of anchors. This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost. In this paper, we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes. We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation. Moreover, based on the recursive least square algorithm, we present a new formulation of the original DV-Hop localization algorithm, namely, online DV-Hop localization, in WSNs. Finally, different scenarios of path planning and simulation results are conducted.

Research paper thumbnail of Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accura... more Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accurate sensor localization is an important aspect of the acquired data. While connectivity algorithms are commonly used for localizing multi-hop WSNs because their simplicity and acceptable accuracy, their effectiveness can be limited in two-dimensional (2D) or three-dimensional (3D) environments. An analytic model that incorporates hop size quantization and the Recursive Least Squares (RLS) method can be advantageous for Range-Free 3D wireless sensor networks (WSNs) in localization. This approach reduces computational complexity, memory requirements, and localization errors. The third dimension significantly impacts localization accuracy, necessitating the development of effective self-localization algorithms for 3D WSNs. This article introduces a novel probabilistic quantization technique for hop sizes in 3D-WSNs, specifically designed to address the uniform distribution of sensor nodes. T...

Research paper thumbnail of Lightweight Neural Networks for Pedestrian Detection in Intelligent Vehicles

Advances in computer and electrical engineering book series, Mar 3, 2023

Research paper thumbnail of An Embedded Implementation of a Traffic Light Detection System for Advanced Driver Assistance Systems

Industrial Transformation, 2022

Research paper thumbnail of Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

International Journal of Informatics and Communication Technology (IJ-ICT)

Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermor... more Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous...

Research paper thumbnail of Traffic Sign Detection for Smart Public Transport Vehicles: Cascading Convolutional Autoencoder With Convolutional Neural Network

Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges

Traffic sign detection is one of the most important tasks for autonomous public transport vehicle... more Traffic sign detection is one of the most important tasks for autonomous public transport vehicles. It provides a global view of the traffic signs on the road. In this chapter, we introduce a traffic sign detection method based on auto-encoders and Convolutional Neural Networks. For this purpose, we propose an end-to-end unsupervised/supervised learning method to solve a traffic sign detection task. The main idea of the proposed approach aims to perform an interconnection between an auto-encoder and a Convolutional Neural Networks to act as a single network to detect traffic signs under real-world conditions. The auto-encoder enhances the resolution of the input images and the convolutional neural network was used to detect and identify traffic signs. Besides, to build a traffic signs detector with high performance, we proposed a new traffic sign dataset. It contains more classes than the existing ones, which contain 10000 images from 73 traffic sign classes captured on the Chinese ...

Research paper thumbnail of Traffic Sign recognition for smart vehicles based on lightweight CNN implementation on mobile devices

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

Research paper thumbnail of An edge implementation of a traffic sign detection system for Advanced driver Assistance Systems

International Journal of Intelligent Robotics and Applications

Research paper thumbnail of Traffic Sign Detection for Green Smart Public Transportation Vehicles Based on Light Neural Network Model

Computational Intelligence Techniques for Green Smart Cities

Research paper thumbnail of Embedded Real Time Operating Systems For Dynamic Reconfiguration

The complexity of embedded systems is growing rapidly and demand for new approaches to meet the n... more The complexity of embedded systems is growing rapidly and demand for new approaches to meet the needs of businesses. the design of SoC and SOPC becomes increasingly complex as they incorporate more and more IP and peripheral controllers such as HDMI, Ethernet, wireless controller. For this reason the presence of an operating system is essential to manage all these features. This paper is the state of the art reconfigurable hardware operating systems. It addresses several aspects of dynamic reconfiguration and presents implementation issues associated with implementation. Aspects communications, scheduling and placement tasks are described and solutions are presented.

Research paper thumbnail of On-line Sequential ELM based localization process for large scale Wireless Sensors Network

2021 International Conference on Control, Automation and Diagnosis (ICCAD)

In most cases of Wireless Sensor Network (WSN) application, the event information transmitted via... more In most cases of Wireless Sensor Network (WSN) application, the event information transmitted via the connected wireless sensor has not great significance without a precise valuation of its geographical position. In this work, we exploit the Machine Learning Technique (MLT) to improve node localization accuracy in WSN. The adopted MLT was applied with a range-free technique which is founded on the multi-hop localization process. The proposed methods are based on the Extreme Learning Machine (ELM) and the On-line Sequential Extreme Learning Machine. Simulation results demonstrate that the proposed algorithms permit to greatly minimize the average localization errors of the estimated node’s position compared to the basis ELM learning machine and the well know Distance Vector Hop (Dv-Hop). Satisfactory results were obtained for isotropic and anisotropic environments in range free case.

Research paper thumbnail of Traffic Sign Recognition Based On Scaled Convolutional Neural Network For Advanced Driver Assistance System

2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), 2020

Advanced driver assistance system (ADAS) is one of the most important systems for human assistanc... more Advanced driver assistance system (ADAS) is one of the most important systems for human assistance. It assists the drivers to control the vehicle by providing essential information about the environment objects. In this paper, we propose a traffic signs recognition application for ADAS. The proposed application is based on the deep learning technique. In particular, we used the convolutional neural networks (CNN) to process the data provided by the system cameras. The proposed CNN was scaled in a way to get a light model size without decreasing the accuracy. The proposed CNN is suitable for embedded implementation while keeping high performance and real-time processing. The evaluation of the proposed CNN on the European dataset results in 99.32% accuracy and 250 FPS of inference speed when implemented on an Nvidia GTX960 GPU. The achieved results proved the efficiency of the scaling technique. It is a very good technique to get a small model size and high performance.

Research paper thumbnail of A study of the color-structure descriptor for shot boundary detection

sta-tn.com

Abstract. This paper focus on the study of the color structure descriptor (CSD) for shot boundary... more Abstract. This paper focus on the study of the color structure descriptor (CSD) for shot boundary detection in video sequences. We interest in the validation and the optimisation of this descriptor in the aim of its real time implementation on hardware architecture. In this ...

Research paper thumbnail of Etudes des nouvelles solutions de mise en œuvre de la reconfiguration dynamique partielle à travers un exemple d’application pratique

Research paper thumbnail of Efficient Serial Architecture for PRESENT Block Cipher

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

Research paper thumbnail of FPGA-based SOC for hardware implementation of a local histogram-based video shot detector

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2017

In this paper, we present a video application example and its implementation in a reconfigurable ... more In this paper, we present a video application example and its implementation in a reconfigurable system-onchip (SOC) platform. The proposed platform employs the benefits of field programmable gate array (FPGA) technology. A prototype based on a Xilinx Virtex-5 FPGA is developed. The application includes a video shot boundary detection module based on the local histogram (LH) technique. Diverse hardware module versions corresponding to different quantization levels and architectural solutions for an LH-based shot detection system are presented. The developed modules have different hardware resource occupations and can be used in a dynamic way to allow flexible management of the target hardware system. They also show high execution time efficiency and can reach an important bandwidth that can support the most recent high-resolution video formats with a high rate of frames per second. A complete SOC-based demonstration for video summarization based on the LH is also developed. It includes the LH extraction, shot boundary detection, and key frame visualization.

Research paper thumbnail of An Iterative Method for Algorithms Implementation on a Limited Dynamically Reconfigurable Hardware

Journal of Computer Science, 2006

In this study we propose a framework and a combined temporal partitioning and designspace explora... more In this study we propose a framework and a combined temporal partitioning and designspace exploration method for run time reconfigurable processors. Our objective is to help designers toimplement an algorithm in limited FPGA area resources while respecting the execution time constraint.The algorithm to be implemented is represented by a task graph with different implementationalternatives (design points) for each task. We study the effect of hardware resources limitation in thechoice of the algorithm implementation design point. The proposed method is based on an heuristictechnique which consists on combining temporal partitioning and task design points selection to obtainsolutions that satisfy the imposed constraints.

Research paper thumbnail of Node Localization in Range-Free 3D-WSNs Using New DV-Hop Algorithm Based Machine Learning Techniques

Research paper thumbnail of Cascade Machines Learning Process for Node Localization in Large-Scale Wireless Sensor Networks

Ingénierie Des Systèmes D'information, Aug 31, 2022

Localization is a crucial concern in many Wireless Sensor Network (WSN) applications. Moreover, g... more Localization is a crucial concern in many Wireless Sensor Network (WSN) applications. Moreover, getting accurate information about geographic positions of nodes (sensors) is very interesting to make the collected data useful and meaningful. The based connectivity algorithms aim to localize multi-hop WSN thanks to their advantages such as their simplicity and acceptable accuracy. However, the localization accuracy may be relatively low due to environment conditions. An Extreme Learning Machine technique (ELM) is given in this manuscript to minimize the localization error in Range-Free WSN. In this work, based on the Cascade-ELM, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to improve the localization accuracy in Range-Free WSN. We applied the proposed methods in different scenarios of Multi-hop WSN. In our study, we focused on an isotropic and irregular environment. Simulation results prove that the proposed Cascade-ELM algorithm greatly optimizes the localization accuracy in comparison with other algorithms issued from smart computing techniques. Improved localization performances, when compared to previous works, are obtained for isotropic environments.

Research paper thumbnail of Optimized Piccolo Lightweight Block Cipher: Area Efficient Implementation

Traitement Du Signal, Jun 30, 2022

Piccolo algorithm is one of the lightweight block ciphers designed specifically for lowresource d... more Piccolo algorithm is one of the lightweight block ciphers designed specifically for lowresource devices which present physical constraints in terms of area, power, and memory. Various hardware architectures for Piccolo block cipher have been proposed in recent years with the aim of obtaining a more appropriate low-resource design for specific constrained applications. The latter must meet real-time processing constraints without affecting the need for hardware resources. Finding a good compromise between computation time and implementation resource consumption is a major consideration in the design process. In this paper, we suggest six serial hardware architectures for Piccolo lightweight algorithm with a 128 bits key length. Proposed architectures are compared to existing designs based on hardware resource occupancy, latency, and throughput. Also, we tested the security of the Piccolo algorithm, and the obtained results show the good robustness of the Piccolo block cipher against statistical attacks. Thus, we can use the Piccolo algorithm in lightweight applications that require a high level of privacy.

Research paper thumbnail of New Online DV-Hop Algorithm via Mobile Anchor for Wireless Sensor Network Localization

Tsinghua Science & Technology, Oct 1, 2023

In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of u... more In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of users. Moreover, determining where and when that event occurs is crucial; thus, the positions of nodes must be identified. Subsequently, in a range-free case, the Distance Vector-Hop (DV-Hop) heuristic is the commonly used localization algorithm because of its simplicity and low cost. The DV-Hop algorithm consists of a set of reference nodes, namely, anchors, to periodically broadcast their current positions and assist nearby unknown nodes during localization. Another potential solution includes the use of only one mobile anchor instead of these sets of anchors. This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost. In this paper, we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes. We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation. Moreover, based on the recursive least square algorithm, we present a new formulation of the original DV-Hop localization algorithm, namely, online DV-Hop localization, in WSNs. Finally, different scenarios of path planning and simulation results are conducted.

Research paper thumbnail of Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accura... more Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accurate sensor localization is an important aspect of the acquired data. While connectivity algorithms are commonly used for localizing multi-hop WSNs because their simplicity and acceptable accuracy, their effectiveness can be limited in two-dimensional (2D) or three-dimensional (3D) environments. An analytic model that incorporates hop size quantization and the Recursive Least Squares (RLS) method can be advantageous for Range-Free 3D wireless sensor networks (WSNs) in localization. This approach reduces computational complexity, memory requirements, and localization errors. The third dimension significantly impacts localization accuracy, necessitating the development of effective self-localization algorithms for 3D WSNs. This article introduces a novel probabilistic quantization technique for hop sizes in 3D-WSNs, specifically designed to address the uniform distribution of sensor nodes. T...

Research paper thumbnail of Lightweight Neural Networks for Pedestrian Detection in Intelligent Vehicles

Advances in computer and electrical engineering book series, Mar 3, 2023

Research paper thumbnail of An Embedded Implementation of a Traffic Light Detection System for Advanced Driver Assistance Systems

Industrial Transformation, 2022

Research paper thumbnail of Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

International Journal of Informatics and Communication Technology (IJ-ICT)

Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermor... more Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous...

Research paper thumbnail of Traffic Sign Detection for Smart Public Transport Vehicles: Cascading Convolutional Autoencoder With Convolutional Neural Network

Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges

Traffic sign detection is one of the most important tasks for autonomous public transport vehicle... more Traffic sign detection is one of the most important tasks for autonomous public transport vehicles. It provides a global view of the traffic signs on the road. In this chapter, we introduce a traffic sign detection method based on auto-encoders and Convolutional Neural Networks. For this purpose, we propose an end-to-end unsupervised/supervised learning method to solve a traffic sign detection task. The main idea of the proposed approach aims to perform an interconnection between an auto-encoder and a Convolutional Neural Networks to act as a single network to detect traffic signs under real-world conditions. The auto-encoder enhances the resolution of the input images and the convolutional neural network was used to detect and identify traffic signs. Besides, to build a traffic signs detector with high performance, we proposed a new traffic sign dataset. It contains more classes than the existing ones, which contain 10000 images from 73 traffic sign classes captured on the Chinese ...

Research paper thumbnail of Traffic Sign recognition for smart vehicles based on lightweight CNN implementation on mobile devices

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

Research paper thumbnail of An edge implementation of a traffic sign detection system for Advanced driver Assistance Systems

International Journal of Intelligent Robotics and Applications

Research paper thumbnail of Traffic Sign Detection for Green Smart Public Transportation Vehicles Based on Light Neural Network Model

Computational Intelligence Techniques for Green Smart Cities

Research paper thumbnail of Embedded Real Time Operating Systems For Dynamic Reconfiguration

The complexity of embedded systems is growing rapidly and demand for new approaches to meet the n... more The complexity of embedded systems is growing rapidly and demand for new approaches to meet the needs of businesses. the design of SoC and SOPC becomes increasingly complex as they incorporate more and more IP and peripheral controllers such as HDMI, Ethernet, wireless controller. For this reason the presence of an operating system is essential to manage all these features. This paper is the state of the art reconfigurable hardware operating systems. It addresses several aspects of dynamic reconfiguration and presents implementation issues associated with implementation. Aspects communications, scheduling and placement tasks are described and solutions are presented.

Research paper thumbnail of On-line Sequential ELM based localization process for large scale Wireless Sensors Network

2021 International Conference on Control, Automation and Diagnosis (ICCAD)

In most cases of Wireless Sensor Network (WSN) application, the event information transmitted via... more In most cases of Wireless Sensor Network (WSN) application, the event information transmitted via the connected wireless sensor has not great significance without a precise valuation of its geographical position. In this work, we exploit the Machine Learning Technique (MLT) to improve node localization accuracy in WSN. The adopted MLT was applied with a range-free technique which is founded on the multi-hop localization process. The proposed methods are based on the Extreme Learning Machine (ELM) and the On-line Sequential Extreme Learning Machine. Simulation results demonstrate that the proposed algorithms permit to greatly minimize the average localization errors of the estimated node’s position compared to the basis ELM learning machine and the well know Distance Vector Hop (Dv-Hop). Satisfactory results were obtained for isotropic and anisotropic environments in range free case.

Research paper thumbnail of Traffic Sign Recognition Based On Scaled Convolutional Neural Network For Advanced Driver Assistance System

2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), 2020

Advanced driver assistance system (ADAS) is one of the most important systems for human assistanc... more Advanced driver assistance system (ADAS) is one of the most important systems for human assistance. It assists the drivers to control the vehicle by providing essential information about the environment objects. In this paper, we propose a traffic signs recognition application for ADAS. The proposed application is based on the deep learning technique. In particular, we used the convolutional neural networks (CNN) to process the data provided by the system cameras. The proposed CNN was scaled in a way to get a light model size without decreasing the accuracy. The proposed CNN is suitable for embedded implementation while keeping high performance and real-time processing. The evaluation of the proposed CNN on the European dataset results in 99.32% accuracy and 250 FPS of inference speed when implemented on an Nvidia GTX960 GPU. The achieved results proved the efficiency of the scaling technique. It is a very good technique to get a small model size and high performance.

Research paper thumbnail of A study of the color-structure descriptor for shot boundary detection

sta-tn.com

Abstract. This paper focus on the study of the color structure descriptor (CSD) for shot boundary... more Abstract. This paper focus on the study of the color structure descriptor (CSD) for shot boundary detection in video sequences. We interest in the validation and the optimisation of this descriptor in the aim of its real time implementation on hardware architecture. In this ...

Research paper thumbnail of Etudes des nouvelles solutions de mise en œuvre de la reconfiguration dynamique partielle à travers un exemple d’application pratique

Research paper thumbnail of Efficient Serial Architecture for PRESENT Block Cipher

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

Research paper thumbnail of FPGA-based SOC for hardware implementation of a local histogram-based video shot detector

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2017

In this paper, we present a video application example and its implementation in a reconfigurable ... more In this paper, we present a video application example and its implementation in a reconfigurable system-onchip (SOC) platform. The proposed platform employs the benefits of field programmable gate array (FPGA) technology. A prototype based on a Xilinx Virtex-5 FPGA is developed. The application includes a video shot boundary detection module based on the local histogram (LH) technique. Diverse hardware module versions corresponding to different quantization levels and architectural solutions for an LH-based shot detection system are presented. The developed modules have different hardware resource occupations and can be used in a dynamic way to allow flexible management of the target hardware system. They also show high execution time efficiency and can reach an important bandwidth that can support the most recent high-resolution video formats with a high rate of frames per second. A complete SOC-based demonstration for video summarization based on the LH is also developed. It includes the LH extraction, shot boundary detection, and key frame visualization.

Research paper thumbnail of An Iterative Method for Algorithms Implementation on a Limited Dynamically Reconfigurable Hardware

Journal of Computer Science, 2006

In this study we propose a framework and a combined temporal partitioning and designspace explora... more In this study we propose a framework and a combined temporal partitioning and designspace exploration method for run time reconfigurable processors. Our objective is to help designers toimplement an algorithm in limited FPGA area resources while respecting the execution time constraint.The algorithm to be implemented is represented by a task graph with different implementationalternatives (design points) for each task. We study the effect of hardware resources limitation in thechoice of the algorithm implementation design point. The proposed method is based on an heuristictechnique which consists on combining temporal partitioning and task design points selection to obtainsolutions that satisfy the imposed constraints.