Processor Speed Control for Power Reduction of Real-Time Systems

Reducing energy consumption is a critical issue in the design of battery-powered real time systems to prolong battery life. With dynamic voltage scaling (DVS) processors, energy consumption can be reduced efficiently by making appropriate decisions on the processor speed/voltage during the scheduling of real time tasks. Scheduling decision is usually based on parameters which are assumed to be crisp. However, in many circumstances the values of these parameters are vague. The vagueness of parameters suggests that to develop a fuzzy logic approach to reduce energy consumption by determining the appropriate supply-voltage/speed of the processor provided that timing constraints are guaranteed. Intensive simulated experiments and qualitative comparisons with the most related literature have been conducted in the context of dependent real-time tasks. Experimental results have shown that the proposed fuzzy scheduler saves more energy and creates feasible schedules for real time tasks. It also considers tasks priorities which cause higher system utilization and lower deadline miss time

Neural Networks for Flow Bottom Hole Pressure Prediction

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps.  However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells.  The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy

Heuristic Approach for Scheduling Dependent Real-Time Tasks

Reducing energy consumption is a critical issue in the design of battery-powered real time systems to prolong battery life. With dynamic voltage scaling (DVS) processors, energy consumption can be reduced efficiently by making appropriate decisions on the processor speed/voltage during the scheduling of real time tasks. Scheduling decision is usually based on parameters which are assumed to be crisp. However, in many circumstances the values of these parameters are vague. The vagueness of parameters suggests that to develop a fuzzy logic approach to reduce energy consumption by determining the appropriate supply-voltage/speed of the processor provided that timing constraints are guaranteed. Intensive simulated experiments and qualitative comparisons with the most related literature have been conducted in the context of dependent real-time tasks. Experimental results have shown that the proposed fuzzy scheduler saves more energy and creates feasible schedules for real time tasks. It also considers tasks priorities which cause higher system utilization and lower deadline miss time

Performance Enhancement of Multicore Architecture

Multicore processors integrate several cores on a single chip. The fixed architecture of multicore platforms often fails to accommodate the inherent diverse requirements of different applications. The permanent need to enhance the performance of multicore architecture motivates the development of a dynamic architecture. To address this issue, this paper presents new algorithms for thread selection in fetch stage. Moreover, this paper presents three new fetch stage policies, EACH_LOOP_FETCH, INC-FETCH, and WZ-FETCH, based on Ordinary Least Square (OLS) regression statistic method. These new fetch policies differ on thread selection time which is represented by instructions’ count and window size. Furthermore, the simulation multicore tool, , is adapted to cope with multicore processor dynamic design by adding a dynamic feature in the policy of thread selection in fetch stage. SPLASH2, parallel scientific workloads, has been used to validate the proposed adaptation for multi2sim. Intensive simulated experiments have been conducted and the obtained results show that remarkable performance enhancements have been achieved in terms of execution time and number of instructions per second produces less broadcast operations compared to the typical algorithm

Heuristic Approach for Scheduling Dependent Real-Time Tasks

Reducing energy consumption is a critical issue in the design of battery-powered real time systems to prolong battery life. With dynamic voltage scaling (DVS) processors, energy consumption can be reduced efficiently by making appropriate decisions on the processor speed/voltage during the scheduling of real time tasks. Scheduling decision is usually based on parameters which are assumed to be crisp. However, in many circumstances the values of these parameters are vague. The vagueness of parameters suggests that to develop a fuzzy logic approach to reduce energy consumption by determining the appropriate supply-voltage/speed of the processor provided that timing constraints are guaranteed. Intensive simulated experiments and qualitative comparisons with the most related literature have been conducted in the context of dependent real-time tasks. Experimental results have shown that the proposed fuzzy scheduler saves more energy and creates feasible schedules for real time tasks. It also considers tasks priorities which cause higher system utilization and lower deadline miss time

An Efficient Cache Organization for On-Chip Multiprocessor Networks

To meet the growing computation-intensive applications and the needs of low-power, high-performance systems, the number of computing resources in single-chip has enormously increased. By adding many computing resources to build a system in System-on-Chip, its interconnection between each other becomes another challenging issue. In most System-on-Chip applications, a shared bus interconnection which needs an arbitration logic to serialize several bus access requests, is adopted to communicate with each integrated processing unit because of its low-cost and simple control characteristics. This paper focuses on the interconnection design issues of area, power and performance of chip multi-processors with shared cache memory. It shows that having shared cache memory contributes to the performance improvement, however, typical interconnection between cores and the shared cache using crossbar occupies most of the chip area, consumes a lot of power and does not scale efficiently with increased number of cores. New interconnection mechanisms are needed to address these issues. This paper proposes an architectural paradigm in an attempt to gain the advantages of having shared cache with the avoidance of penalty imposed by the crossbar interconnect. The proposed architecture achieves smaller area occupation allowing more space to add additional cache memory. It also reduces power consumption compared to the existing crossbar architecture. Furthermore, the paper presents a modified cache coherence algorithm called Tuned-MESI. It is based on the typical MESI cache coherence algorithm however it is tuned and tailored for the suggested architecture. The achieved results of the conducted simulated experiments show that the developed architecture produces less broadcast operations compared to the typical algorithm

On an extension of kummer-type II transformation

In the theory of hypergeometric and generalized hypergeometric series, Kummer’s type I and II transformations play an important role. In this short research paper, we aim to establish the explicit expression of e⁻ x/2 2F2 [a, d + n; x 2a + n, d;] for n = 3. For n = 0, we have the well known Kummer’s second transformation. For n = 1, the result was established by Rathie and Pogany [12] and later on by Choi and Rathie[2]. For n = 2, the result was recently established by Rakha, et al. [10]. The result is derived with the help of Kummer’s second transformation and its contiguous results recently obtained by Kim, et. al.[4]. The result established in this short research paper is simple, interesting, easily established and may be potentially useful.Publisher's Versio

Adaptive co-operative mobile robots

This work proposes a biologically inspired collective behaviour for a team of co-operating robots. Collective behaviour is achieved by controlling the local interactions among a set of identical mobile robots, each robot performing a set of simple behaviours in order to realise group goals. A modification of the subsumption architecture is proposed for implementing control of individual robots. This architecture is adopted because it is computationally inexpensive and potentially suitable for low-level reactive and reflexive behaviours. In this scenario, the individual behaviours of the robots have different aims, which may cause conflict. To address this issue, a fuzzy logic-based approach for multiple behaviour coordination within each robot is proposed. The work also focuses on the development of intelligent multi-agent robot teams capable of acting autonomously and of collaborating in a dynamic environment to achieve team objectives. A knowledge-based software architecture is proposed that enables these robots to select co-operative behaviours and to adapt their performance during the specified time of the mission. These abilities are important because of uncertainties in the environmental conditions and because of possible functional failures in some team members. Improvement in team performance is achieved by updating the control of the robots based on knowledge acquired on-line. This architecture is implemented in a simulated team of mobile robots performing a proof-of-concept collaborative task. The results show a significant improvement in overall group performance and the robot team is able to achieve adaptive cooperative control despite dynamic changes in the environment and variation in the capabilities of the team members. Finally, a task involving real mobile robots is undertaken to demonstrate a practical, though simplified, implementation of the proposed collective behaviour