A survey of behavioral-level partitioning systems

Many approaches have been developed to partition a system's behavioral description before a structural implementation is synthesized. We highlight the foundations and motivations for behavioral partitioning. We survey behavioral partitioning approaches, discussing abstraction levels, goals, major steps, and key assumptions in each

A multi-objective approach to indoor wireless heterogeneous networks planning

We present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi Access Points (APs) and Long Term Evolution (LTE) femtocells. We propose a design framework based on Multi-objective Biogeography-based Optimization (MOBBO). The results of the proposed method indicate the advantages and applicability of the multi-objective approach

Optimizing Airport Land Side Operations: Check-In, Passengers' Migration, and Security Control Processes

This paper deals with the optimization of the Check-in, passenger migration, and Security Control processes in an airport land side terminal. Given the layout of the terminal, the passengers' flow, and the scheduled flights in a given time interval, the number and the position of Check-in counters and Security Control gates to be opened are output. The objective function is the minimization of the costs to activate the Check-in counters and the Security Control gates plus the costs that measure the passengers' discomfort. The stochastic passengers' behaviour and their preferences are simulated by a discrete event model, while the managing costs and the passengers' discomfort are optimized by the Surrogate Method. Capodichino Airport, located in Naples (IT), has been considered for the test phase. Results show the effectiveness and efficiency of the solutions of the Surrogate Method compared with the performances of other algorithms

Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm

Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems. We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort

Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging

Physics-Based Electromigration Modeling and Analysis and Optimization

Long-term reliability is a major concern in modern VLSI design. Literature has shown that reliability gets worse as technology advances. It is expected that the future VLSI systems would have shorter reliability-induced lifetime comparing with previous generations. Being one of the most serious reliability effects, electromigration (EM) is a physical phenomenon of the migration of metal atoms due to the momentum exchange between atoms and the conducting electrons. It can cause wire resistance change or open circuit and result in functional failure of the circuit. Power-ground networks are the most vulnerable part to EM effect among all the interconnect wires since the current flow on this part is the largest on the chip. With new generation oftechnology node and aggressive design strategies, more accurate and efficient EM models are required. However, traditional EM approaches are very conservative and cannot meet current aggressive design strategies. Besides circuit level, EM also need to be thoroughly studied in system level due to limited power and temperature budgets among cores on chip. This research focuses on developing physical level EM model for VLSI circuits and system level EM optimization for multi-core systems in order to overcome the aforementioned problems. Specifically, for physical level, we develop two EM immortality check methods and a power grid EM check method. Firstly, a voltage based EM immortality analysis has been developed. Immortality condition in nucleation phase can be determined fast and accurately for multi-segment interconnect wires. Secondly, a saturation volume based incubation phase immortality check method has been proposed. This method can further reduce the redundancy in VLSI circuit design by immortality check in multiphase. Furthermore, both immortality check methods are integrated into a new power grid EM check methodology (EMspice) as filter for EM analysis. These filters can accelerate the simulation by filtering out immortal trees so that we only need to do simulation on fewer trees that are mortal. Coupled EM simulation considering both hydrostatic stress and electronic current/voltage in the power grid network will be applied to these mortal trees. This tool can work seamlessly with commercial synthesis flow. Besides physical level reliability models, system level reliability optimization is also discussed in this research. A deep reinforcement learning based EM optimization has been proposed for multi-core system. Both long term reliability effect (hard error) and transient soft error are considered. Energy can be optimized with all the reliability and other constraints fast and accurately compared to existing reliability management techniques. Last but not least, a scheduling based reliability optimization method for multi-core systems has been proposed. NBTI, HCI and EM are considered jointly. Lifetime of the system can be improved significantly compared to traditional methods which mainly focus on utilization

An Empirical Methodology for Engineering Human Systems Integration

The systems engineering technical processes are not sufficiently supported by methods and tools that quantitatively integrate human considerations into early system design. Because of this, engineers must often rely on qualitative judgments or delay critical decisions until late in the system lifecycle. Studies reveal that this is likely to result in cost, schedule, and performance consequences. This dissertation presents a methodology to improve the application of systems engineering technical processes for design. This methodology is mathematically rigorous, is grounded in relevant theory, and applies extant human subjects data to critical systems development challenges. The methodology is expressed in four methods that support early systems engineering activities: a requirements elicitation method, a function allocation method, an input device design method, and a display layout design method. These form a coherent approach to early system development. Each method is separately discussed and demonstrated using a prototypical system development program. In total, this original and significant work has a broad range of systems engineer applicability to improve the engineering of human systems integration