A cost function based optimization technique for scheduling in data path synthesis

Two identification techniques, which are the Method of Feasible Directions (MFD) based on optimization concept and the Hybrid Method (HM) combining the merits of the State Variable Filter method (SVF) and MFD, are proposed for the parameters identification of rotor system with squeeze-film damper (SFD). The parameters of SFD, including hydrodynamic inertia, damping and stiffness, are identified and the results obtained by using MFD and HM are compared to those using SVF. The accuracy and efficiency of using these techniques are demonstrated by the experimental simulation with various noise levels and different initial values. The results indicate that the choice of initial values is of no significant effect on SVF method and the accuracy of SVF depends on the level of noise. However, for MFD method, it is almost independent of noise effect but significantly affected by the choice of initial values. The Hybrid Method (HM) is proposed to overcome these handicaps and found of better accuracy and efficiency than SVF and MFD. Then, it is highly recommended for the parameters identification of system with noise effect.

The main objective of this study is to discuss the optimum correction of linear inequality systems and absolute value equations (AVE). In this work, a simple and efficient feasible direction method will be provided for solving two fractional nonconvex minimization problems that result from the optimal correction of a linear system. We will show that, in some special-but frequently encountered-cases, we can solve convex optimization problems instead of not-necessarily-convex fractional problems. And, by using the method of feasible directions, we solve the optimal correction problem. Some examples are provided to illustrate the efficiency and validity of the proposed method.

Based on the semidefinite programming relaxation of the design of FIR digital filters with SP2 coefficients, a feasible direction method is presented. Coupled with a randomized method, and a suboptimal solution is obtained for the problem. Furthermore, its convergence result is given. Simulation results demonstrate that the feasible direction method is an efficient method.

In this paper, we study the memory allocation problem in data path synthesis. We propose a register organization called circular FIFO as an alternative to register file organization. In comparison with register file organization, FIFO organization eliminates the overhead of address generation and decoding hardware. The memory allocation problem, based on the circular FIFO organization, has been solved efficiently using constraint logic and interval constraint programming.

A new algorithm named the time-zone algorithm for solving register allocation problem in the automatic data path synthesis is presented in this paper. The time-zone algorithm was developed from the left-edge algorithm with which the number of registers can be minimized. The time-zone algorithm was developed to minimize the number of registers as well as the interconnections and the number of multiplexers required for the registers. The experimental results obtained from testing several published examples show that the interconnections are improved and the number of multiplexers is reduced while at the same time the number of registers is still minimized.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Complexity of fragmentable object bin packing and an application

A continuous approach using NCP function for approximating the solution of the max-cut problem is proposed. The max-cut problem is relaxed into an equivalent nonlinearly constrained continuous optimization problem and a feasible direction method without line searches is presented for generating an optimal solution of the relaxed continuous optimization problem. The convergence of the algorithm is proved. Numerical experiments and comparisons on some max-cut test problems show that we can get the satisfactory solution of max-cut problems with less computation time. Furthermore, this is the first time that the feasible direction method is combined with NCP function for solving max-cut problem, and similar idea can be generalized to other combinatorial optimization problems.

We address the problem of minimizing power consumption in behavioral synthesis of data-dominated circuits. The complex nature of power as a cost function implies that the effects of several behavioral synthesis tasks like module selection, clock selection, scheduling, and resource sharing on supply voltage and switched capacitance need to be considered simultaneously to fully derive the benefits of design space exploration at the behavior level. Recent work has established the importance of behavioral synthesis in low power VLSI design. However, most of the algorithms that have been proposed separate these tasks and perform them sequentially, and are hence not able to explore the tradeoffs possible due to their interaction. We present an efficient algorithm for performing scheduling, clock selection, module selection, and resource allocation and assignment simultaneously with an aim of reducing the power consumption in the synthesized data path. The algorithm, which is based on an iterative improvement strategy, is capable of escaping local minima in its search for a low power solution. The algorithm considers diverse module libraries and complex scheduling constructs such as multicycling, chaining, and structural pipelining. We describe supply voltage and clock pruning strategies that significantly improve the efficiency of our algorithm by cutting down on the computational effort involved in exploring candidate supply voltages and clock periods that are unlikely to lead to the best solution. Experimental results are reported to demonstrate the effectiveness of the algorithm. Our techniques can be combined with other known methods of behavioral power optimization like data path replication and transformations, to result in a complete data path synthesis system for low power applications.

In this article, we investigate the application of feasible direction method for an optimistic non-linear bilevel programming problem. The convex lower level problem of an optimistic non-linear bilevel programming problem is replaced by relaxed KKT conditions. The feasible direction method developed by Topkis and Veinott [D.M. Topkis and A.F.jun. Veinott, On the convergence of some feasible direction algorithms for nonlinear programming, SIAM J. Control Optim. 5(1967), pp. 268–279] is applied to the auxiliary problem to get a Bouligand stationary point for an optimistic bilevel programming problem.

On-line testable data path synthesis for minimizing testing time

OASIS is a cell-based silicon compiler system that supports rapid implementation of digital Application specific ICs (ASICs). The system automates the design process by taking a high-level language description and producing a layout of a testable IC. OASIS facilitates design of large digital systems on silicon and gives the designer sufficient control to enable exploration of the speed-versus-area design tradeoffs. The front end of OASIS is the LOGIC-III compiler that translates a high-level language description into a hierarchical netlist of gates. The LOGIC-III language is a PASCAL-like language that allows the designer to specify his design completely and in a uniform, consistent manner. The language supports both the logic synthesis for FSMs and combinational logic, and the structural/algorithmic synthesis of data paths and regular structures such as adders or multipliers. The OASIS system comprises an integrated set of tools for complete design, simulation, verification, layout and testing of ASICs. Experience with the system shows a marked increase in design productivity when compared with more traditional design techniques. The authors describe the system with its unique features and present some of the results of using OASIS in designing semi-custom ASICs. >

Commuting matrices are key for a variety of fields, including transportation engineering and urban planning. Up to now, these matrices have been typically generated from data obtained from surveys. Nevertheless, such approaches typically involve high costs which limits the frequency of the studies. Cell phones can be considered one of the main sensors of human behavior due to its ubiquity, and as a such, a pervasive source of mobility information at a large scale. In this paper we propose a new technique for the estimation of commuting matrices using the data collected from the pervasive infrastructure of a cell phone network. Our goal is to show that we can construct cell-phone generated matrices that capture the same patterns as traditional commuting matrices. In order to do so we use optimization techniques in combination with a variation of Temporal Association Rules. Our validation results show that it is possible to construct commuting matrices from call detail records with a high degree of accuracy, and as a result our technique is a cost-effective solution to complement traditional approaches.

This paper presents the feasible direction (FD) method for calculating equilibrium compositions in combustion systems via Gibbs free energy minimization under mass balance constraints. The method was used to calculate the adiabatic flame temperature and the equilibrium composition of combustion products from combustion reactions of two types. The results of the present work are compared with those obtained using the Lagrange multipliers method. The proposed algorithm demonstrated robust convergence at the complicated multicomponent equilibrium calculation involving the condensed products as well as the simple chemical equilibrium problem. In addition, we simulated the equilibrium compositions and thermodynamic properties of combustion products depending on the temperature.

Haplotype inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This piece of information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony) and to solve it using off-the-shelf combinatorial optimization techniques. In this paper, we present and discuss an approach based on hybridization of two meta-heuristics, one being a population based learning algorithm and the other a local search. We test our approach by solving instances from common Haplotype inference benchmarks. Results show that this approach achieves an improvement on solution quality with respect to the application of a single 'pure' algorithm.

In recent years, the introduction of electric vehicles (EVs) has become increasingly desirable from an environmental perspective. However, it is important to develop suitable charging equipment and infrastructure, as EVs tend to have shorter cruising distances than gasoline-powered vehicles. Various data-driven methods have been proposed for the placement of charging stations (CSs). To date, however, there have been few simulation-driven approaches that can take account of the microscale interactions among vehicles. Therefore, in this paper, we propose an efficient placement method based on multi-agent traffic simulations with an improved scoring function. In addition, as the number of CSs increase in proportion to the spread of EVs, we solve a multiple location design problem formulated as a combinatorial optimization technique. Numerical experiments were conducted on a realistic network in the central part of Wakayama Prefecture in Japan, and it was shown that the proposed method could improve the performance indicator of CS placement compared with the conventional method. Furthermore, the scenario of multiple CS installations at one time demonstrated that good combinations could be obtained.