Serag E.-D. Habib

Efficient FPGA Implementation of MIMO Decoder for Mobile WiMAX System

In this paper, we present a FPGA prototyping of the MIMO Decoder for the IEEE 802.16e WiMAX mobile systems. The IEEE 802.16e standard supports three types of MIMO space time codes (STC), referred to in the standard by matrix A, B, and C, that achieve different levels of throughput and diversity depending on the quality of the MIMO channels. In particular, the STC matrix A achieves full diversity by employing the Alomuti coding, while the STC matrix B achieves full rate by employing spatial multiplexing and the STC matrix C achieves full rate and diversity by employing the Golden code. In this paper, we present a FPGA architecture of MIMO decoder based on the fixed sphere decoder (FSD) algorithm that achieves close-to ML BER performance with a reduced computational complexity and fixed throughput. We show how a single FSD can be used to decode the different STC by adaptively processing the received signal according to the STC type prior to be fed to the FSD. The FPGA design is incorporated with a QR decomposition of the channel matrix. The proposed FSD achieves fixed and high throughput required for the WiMAX systems. The FPGA implementation is incorporated with a MATLAB simulation model of an FUSC OFDMA-based WiMAX 2x2 MIMO system to validate the hardware design.

Hardware Software Partitioning using Particle Swarm Optimization Technique

In this paper the authors investigate the application of the particle swarm optimization (PSO) technique for solving the hardware/software partitioning problem. The PSO is attractive for the hardware/software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loops execution time, where n is the number of proposed solutions that will evolve to provide the final solution. The authors carried out several tests on a hypothetical, relatively-large hardware/software partitioning problem using the PSO algorithm as well as the genetic algorithm (GA), which is another evolutionary technique. The authors found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, the authors tested several hybrid combinations of PSO and GA algorithm; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. The paper proposes to name this successive PSO algorithm as the re-excited PSO algorithm

The limiting efficiency of band gap graded solar cells

Two fundamental mechanisms limit the maximum attainable efficiency of solar cells, namely the radiative recombination and Auger recombination. We show in this paper that proper band gap grading of the solar cell localizes the Auger recombination around the metallurgical junction. Two beneficial effects result from this Auger recombination localization; first the cell is less sensitive to the surface conditions, and second, the previous estimates for the limiting efficiency of solar cells by Shockley, Tiedje, and Green are revised upwardly. We calculate the optimum bandgap grading profile for several real material systems, including GaInAsP lattice matched to InP, and a-SiGe on a-Si substrate.

An integrated high-level hardware/software partitioning methodology

Embedded systems are widely used in many sophisticated applications. To speed the time-to-market cycle, the hardware and software co-design has become one of the main methodologies in modern embedded systems. The most important challenge in the embedded system design is partitioning; i.e. deciding which modules of the system should be implemented in hardware and which ones in software. Finding an optimal partition is hard because of the large number and different characteristics of the modules that have to be considered.In this article, we develop a new high-level hardware/software partitioning methodology. Two novel features characterize this methodology. Firstly, the Particle Swarm Optimization (PSO) technique is introduced to the Hardware/Software partitioning field. Secondly, the hardware is modeled using two extreme implementations that bound different hardware scheduling alternatives. Our methodology further partitions the design into hardware and software modules at the early Control-Data Flow Graph (CDFG) level of the design; thanks to improved modeling techniques using intermediate-granularity functional modules. A new restarting technique is applied to PSO to avoid quick convergence. This technique is called Re-Excited PSO. Our numerical results prove the usefulness of the proposed technique.The target technology is Field Programmable Gate Arrays (FPGAs). We developed FPGA-based estimation techniques to evaluate the costs of implementing the design components. These costs are the area, delay, latency, and power consumption for both the hardware and software implementations. Hardware/software communication is also taken into consideration.The aforementioned methodology is embodied in an integrated CAD tool for hardware/software co-design. This tool accepts behavioral, un-timed, algorithmic-level, VHDL, design representation, and outputs a valid hardware/software partition and schedule for the design subject to a set of area/power/delay constraints. This tool is code named CUPSHOP for (Cairo University PSo-based Hardware/sOftware Partitioning tool). Finally, a JPEG-encoder case study is used to validate and contrast our partitioning methodology against the prior-art methodologies.

Efficient multi-feature PSO for fast gray level object-tracking

Robust and real-time moving object tracking is a tricky job in computer vision systems. The development of an efficient yet robust object tracker faces several obstacles, namely: dynamic appearance of deformable or articulated targets, dynamic backgrounds, variation in image intensity, and camera (ego) motion. In this paper, a novel tracking algorithm based on particle swarm optimization (PSO) method is proposed. PSO is a population-based stochastic optimization algorithm modeled after the simulation of the social behavior of bird flocks and animal hordes. In this algorithm, a multi-feature model is proposed for object detection to enhance the tracking accuracy and efficiency. The objects model is based on the gray level intensity. This model combines the effects of different object cases including zooming, scaling, rotating, etc. into a single cost function. The proposed algorithm is independent of object type and shape and can be used for many object tracking applications. Over 30 video sequences and having over 20,000 frames are used to test the developed PSO-based object tracking algorithm and compare it to classical object tracking algorithms as well as previously published PSO-based tracking algorithms. Our results demonstrate the efficiency and robustness of our developed algorithm relative to all other tested algorithms.

Constrained and Unconstrained Hardware-Software Partitioning using Particle Swarm Optimization Technique

In this paper we investigate the application of the Particle Swarm Optimization (PSO) technique for solving the Hardware/Software partitioning problem. The PSO is attractive for the Hardware/Software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loops execution time, where n is the number of proposed solutions that will evolve to provide the final solution. We carried out several tests on a hypothetical, relatively-large Hardware/Software partitioning problem using the PSO algorithm as well as the Genetic Algorithm (GA), which is another evolutionary technique. We found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, we tested several hybrid combinations of PSO and GA algorithms; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. We propose to name this successive PSO algorithm as the Re-excited PSO algorithm. The constrained formulations of the problem are investigated for different tuning or limiting design parameters constraints.

The ALDMOST: A new power MOS transistor

A new lateral power MOSFET structure, named the Accumulation LDMOST (ALDMOST), is proposed. It relies on creation of an accumulation layer along the surface of the drift region. This surface accumulation layer exists only in the ON state. Simulation studies indicate that the product of the ON resistance by the area (R_{on}.A) of the ALDMOST is one-third to one-fifth that of a conventional LDMOST rated at the same breakdown voltage.

Fast Hardware Upper-Bound Power Estimation for a Novel FPGA-Based HW/SW Partitioning Scheme

In this paper a fast and accurate upper-bound power consumption estimation tool for FPGA-based designs is presented. The tool is developed in the context of a HW/SW partitioning tool. Rather than modeling the hardware implementation as a single alternative, our approach for HW/SW partitioning models the hardware as two extreme alternatives that bound the latency range for different hardware implementations. The presented estimation tool estimates the power consumption for these two hardware alternatives. The computational cost of the presented estimation tool depends linearly on the design complexity as no simulation processes are performed, and hence, it is very useful for fast design space exploration. Testing this estimation tool on several designs showed that this tool is also accurate. Overall power consumption estimations are within plusmn4% of the actual power consumed with an average of 1% error. However, Logic Elements (LEs) and clock power estimates are accurate with an average error of 8.25% and 6.25%, respectively.

Fast FPGA-Based Delay Estimation for a Novel Hardware/Software Partitioning Scheme

In this paper a fast and accurate delay estimation tool for FPGA-based designs is presented. The tool is developed in the context of a HW/SW partitioning tool. Rather than modeling the hardware as a single implementation, our approach for HW/SW partitioning models the hardware as two extreme alternatives that bound the area and latency ranges for different hardware implementations. The presented tool estimates the delay for these two hardware alternatives. Our delay modeling technique accounts for both the logic and routing delays so as to minimize the estimation error. The computational cost of the presented estimation tool depends linearly on the design complexity, and hence, it is very useful for fast design space exploration. Testing this estimation tool on several designs showed that this tool is also accurate with an average error of 4.2%.

Particle Swarm Optimization for HW/SW Partitioning

Embedded systems typically consist of application specific hardware parts and programmable parts, e.g. processors like DSPs, core processors or ASIPs. In comparison to the hardware parts, the software parts are much easier to develop and modify. Thus, software is less expensive in terms of costs and development time. Hardware, however, provides better performance. For this reason, a system designers goal is to design a system fulfilling all system constraints. The co-design phase, during which the system specification is partitioned onto hardware and programmable parts of the target architecture, is called Hardware/Software partitioning. This phase represents one key issue during the design process of heterogeneous systems. Some early co-design approaches [Marrec et al. 1998, Cloute et al. 1999] carried out the HW/SW partitioning task manually. This manual approach is limited to small design problems with small number of constituent modules. Additionally, automatic Hardware/Software partitioning is of large interest because the problem itself is a very complex optimization problem. Varieties of Hardware/Software partitioning approaches are available in the literature. Following Nieman [1998], these approaches can be distinguished by the following aspects: 1. The complexity of the supported partitioning problem, e.g. whether the target architecture is fixed or optimized during partitioning. 2. The supported target architecture, e.g. single-processor or multi-processor, ASIC or FPGA-based hardware. 3. The application domain, e.g. either data-flow or control-flow dominated systems. 4. The optimization goal determined by the chosen cost function, e.g. hardware minimization under timing (performance) constraints, performance maximization under resource constraints, or low power solutions. 5. The optimization technique, including heuristic, probabilistic or exact methods, compared by computation time and the quality of results. 6. The optimization aspects, e.g. whether communication and/or hardware sharing are taken into account. 7. The granularity of the pieces for which costs are estimated for partitioning, e.g. granules at the statement, basic block, function, process or task level. 8. The estimation method itself, whether the estimations are computed by special estimation tools or by analyzing the results of synthesis tools and compilers.