Mohammed Moness

A high performance algorithm for scheduling and hardware-software partitioning on MPSoCs

Multi-processor system-on-chip (MPSoC) is an integrated circuit containing multiple cores that implements most of the functionality of a complex electronic system and some other components like FPGA/ASIC on single chip. The most crucial things in such like these systems are the performance, energy, power and area optimization. Moreover, scheduling the tasks of an application on to the processors (cores) and HW/SW partitioning are inter-dependent in the traditional design space exploration process. In this paper, we propose an algorithm to produce the optimality of scheduling and optimize the number of cores that can be used and also reduce the overall execution time and number of buses on the chip by using efficient hardwaresoftware co-design partitioning technique. The viability and potential of the proposed algorithm is demonstrated by extensive experimental results to conclude that the proposed algorithm is an efficient scheme to obtain the optimality of scheduling and partitioning with hard and large task graph problems.

A Survey of Cyber-Physical Advances and Challenges of Wind Energy Conversion Systems: Prospects for Internet of Energy

Wind energy has the biggest market share of renewable energy around the world. High growth and development rates of wind energy lead to a massive increase in complexity and scale of wind energy conversion systems (WECSs). Therefore, it is required to upgrade methods and strategies for design and implementation of WECS. Considering WECS as cyber-physical systems (CPSs) will enable wind energy for the Internet of Energy (IoE). IoE is a cloud network where power sources with embedded and distributed intelligence are interfaced to smart grid and mass of consumption devices like smart buildings, appliances, and electric vehicles. Research trends of CPS for energy applications are mainly focusing on smart grids and energy systems for demand-side management and smart buildings with less attention given to generation systems. This paper introduces potentials of cyber-physical (CP) integration of next-generation WECS. In addition, this paper surveys the advances and state-of-the-art technologies that enable WECS for IoE. Challenges and new requirements of future WECS as CPS like abstractions, networking, control, safety, security, sustainability, and social components are discussed.

MPSoCs and Multicore Microcontrollers for Embedded PID Control: A Detailed Study

This paper presents different multiprocessor implementations of the proportional-integral-derivative (PID) controller using two technologies: 1) field programmable gate array (FPGA)-based multiprocessor system-on-chip (MPSoC); and 2) multicore microcontrollers (MCUs). Techniques to implement a parallelized PID controller, a multi-PID controller, and a self-tuning PID controller are proposed. These techniques are verified using hardware (HW) in the loop (HIL) simulations. Then, the paper presents a detailed case study of an embedded real-time (RT) self-tuning PID controller for a 1-degree-of-freedom (1-DOF) aerodynamical system. This includes controller design, parameters tuning, and implementation using a multiprocessor system. Results proved the effectiveness of the proposed techniques to improve performance and functionality. It is shown that customizing HW and software (SW) within MPSoCs provides higher RT performance. Moreover, using multicore MCUs can reduce design time, implementation time, and cost, while keeping adequate performance. Therefore, it is possible to realize and implement complex RT embedded controllers that employ advanced control algorithms in rapid, effective, and cost-efficient fashion.

Efficient partitioning technique on multiple cores based on optimal scheduling and mapping algorithm

In this paper, efficient hardware-software (HW-SW) partitioning technique based on high performance scheduling and mapping algorithms on multiple cores is presented. The scheduling and mapping algorithms produce the optimality of mapping tasks onto cores. The partitioning technique reduces the overall execution time and number of buses among the cores. The viability and potential of the proposed algorithms are demonstrated by extensive experimental results to conclude that the proposed algorithms are efficient scheme to obtain the optimality of scheduling, mapping and partitioning with hard and large task graph problems.

High performance reconfigurable Viterbi Decoder design for multi-standard receiver

A Viterbi Decoder (VD) is employed to decode the convolutional codes, where convolutional codes are commonly used to encode digital data before transmission. However, there is a large variety of modern wireless communication standards; a flexible hardware platform that can be configured to support different standards is still needed. In this paper, a reconfigurable Viterbi decoder has been designed. The proposed Viterbi decoder has an architecture that supports constraint lengths 3, 5, and 7, and code rates 1/2 and 1/3 which makes it compatible with many common standards, like Wi-Max, WLAN, 3GPP2, GSM and LTE. The proposed Viterbi decoder has been simulated using Xilinx ISE 14.5 simulator and implemented with VHDL on Xilinx Zed board, Zynq-7000 FPGA using Xilinx iMPACT device configuration tool. Moreover, in the proposed architecture design, a modified add-compare-select unit that efficiently reduces power consumption by 26% and area by 21% is employed.

An Efficient Implementation of Ant Colony Optimization on GPU for the Satisfiability Problem

This paper focuses on solving the Boolean Satisfiability (SAT) problem using a parallel implementation of the Ant Colony Optimization (ACO) algorithm for execution on the Graphics Processing Unit (GPU) using NVIDIA CUDA (Compute Unified Device Architecture). We propose a new efficient parallel strategy for the ACO algorithm executed entirely on the CUDA architecture, and perform experiments to compare it with the best sequential version exists implemented on CPU with incomplete approaches. We show how SAT problem can benefit from the GPU solutions, leading to significant improvements in speed-up even though keeping the quality of the solution. Our results shows that the new parallel implementation executes up to 21x faster compared to its sequential counterpart.

Tuning a digital multivariable controller for a lab-scale helicopter system via simulated annealing and evolutionary algorithms

Helicopter systems are considered a complex and challenging control problem due to strong couplings and high non-linearities. In this paper, simulated annealing (SA), as one of the leading methods in search and optimization, is applied to tune a multivariable controller of a lab-scale helicopter system. The lab-scale helicopter system is a multivariable experimental aerodynamic test rig that resembles the behaviour of a real helicopter. The control objectives are quickly to reach a desired position or track a trajectory. A centralized cross-coupled PID controller is used to achieve these objectives. First, SA optimizations are carried out with 24 different initial configurations. Then, the best results of these SA configurations are compared with other controllers obtained with evolutionary algorithms (EAs) of genetic algorithms (GAs), modified particle swarm optimization (MPSO) and differential evolution (DE). The comparisons are based on statistical measures of 20 independent trials, non-linear computer simulations of different input signals and real-time measurements for various commands of positions or trajectories. Results show that SA obtained the best performance index and acceptable time-domain performance on reaching hovering point, following a step command and tracking a sine trajectory compared with the investigated EAs.

Optimization method for scheduling length and the number of processors on multiprocessor systems

A high performance algorithm for scheduling of tasks aims to optimize the overall execution time of the program by properly allocating and arranging the execution order of the tasks on the multiprocessor systems such that the precedence constraints among the tasks are preserved. In this paper, we propose an algorithm to get the optimality of scheduling for large problem sizes and optimize the target system. The algorithm uses geometrical analysis based on an Artificial Intelligence (AI) technique to produce the optimal solution for the allocation/scheduling problem, also it uses pruning techniques to reduce the size of the search space and to minimize the number of processors that used. The viability and potential of the proposed algorithm is demonstrated by extensive experimental results (more than 180 random task graphs) to conclude that the proposed algorithm is an efficient scheme to obtain the optimality with hard and large problem of task graphs.

A Real-Time Heterogeneous Emulator of a High-Fidelity Utility-Scale Variable-Speed Variable-Pitch Wind Turbine

Wind energy has the highest development rates of renewables. The increasing complexity of wind turbine (WT) systems requires careful analysis and design with thorough testing and certification procedures. Hardware emulators contribute to safe and cost-effective assessment and testing of WT in research and industry. Most of the available emulators concentrate on emulating electrical subsystems with simplified mechanical models. In this paper, a real-time (RT) heterogeneous emulator that combines RT discrete-time step simulation and a high-fidelity linear parameter-varying model of a utility-scale WT system is proposed and implemented on a heterogeneous CPU/GPU platform. The RT emulator is built on an embedded NVIDIA Jetson TK1 board for a National Renewable Energy Laboratory 5-MW WT as a case study. The proposed emulator is capable of further integration of electrical models and control systems of WT.

An algorithm for parameter estimation of twin-rotor multi-input multi-output system using trust region optimization methods

The twin-rotor, multi-input, multi-output system is a typical lab-scale helicopter system, since it resembles behavior of helicopter systems. This experimental complex aerodynamic test rig needs precise modeling to assure satisfactory control performance. Therefore, this article is intended to provide a gray-box approach for the modeling of the twin-rotor, multi-input, multi-output system. An algorithm is proposed by dividing the system into independent subsystems that can be estimated for an excitation input or a characteristic response. The proposed algorithm uses trust region optimization methods for successive estimations. The iterative optimization methods of Levenberg–Marquardt, gradient descent active set, and interior point are compared with respect to trust region methods. The model is validated with various sets of scenarios: different inputs, different types of motion, and different operating points. Effect of operating point selection on the parameter estimation process is investigated. Time domain response comparison of the single-degree-of-freedom vertical, single-degree-of-freedom horizontal, and two-degrees-of-freedom models are acceptable.