VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro

this article goes to press. Journal editors can be encouraged to seek out referees who have done rigorous empirical studies. Refereeing standards will evolve, particularly as the empirical science develops.

This paper presents a non-conventional scheduling approach for distributed static systems where tasks are periodic and have arbitrary deadlines, precedence, and exclusion constraints. The solution presented in this work not only creates feasible schedules, but also minimizes jitter for periodic tasks. The problem of scheduling real-time tasks with minimum jitter is particularly important in many control applications, nevertheless, it has been rarely studied in the scientific literature. We present a general framework consisting of an abstract architecture model and a general programming model. We show how to design a surprisingly simple and flexible scheduling method based on simulated annealing and present some experimental results. 1 Introduction Real-time distributed systems are becoming more commonplace. Applications like process control, avionics, and robotics need real-time support to schedule and synchronize real-time tasks running on remote nodes. In those cases where the envi...

To truly exploit FPGAs for rapid turn-around development and prototyping, placement times must be reduced to seconds; latebound, reconfigurable computing applications may demand placement times as short as microseconds. In this paper, we show how a systolic structure can accelerate placement by assigning one processing element to each possible location for an FPGA LUT from a design netlist. We demonstrate that our technique approaches the same quality point as traditional simulated annealing as measured by a simple linear wirelength metric. Experimental results look ahead to compare quality against VPR’s fast placer when considering the minimum channel width required to route as the primary optimization criteria. Preliminary results from an FPGA implementation show the feasibility of accelerating simulated annealing by three orders of magnitude using this approach. This means we can place the largest design in the University of Toronto’s “FPGA

Simulated Annealing (SA) is a powerful stochastic search method applicable to a wide range of problems for which little prior knowledge is available. It can produce very high quality solutions for hard combinatorial optimization problems. However, the computation time required by SA is very large. Various methods have been proposed to reduce the computation time, but they mainly deal with the careful tuning of SA's control parameters. This paper first analyzes the impact of SA's neighbourhood on SA's performance and shows that SA with a larger neighbourhood is better than SA with a smaller one. The paper also gives a general model of SA, which has both dynamic generation probability and acceptance probability, and proves its convergence. All variants of SA can be unified under such a generalization. Finally, a method of extending SA's neighbourhood is proposed, which uses a discrete approximation to some continuous probability function as the generation function in SA, and several impo...

Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations.

this paper, we give a rigorous analysis of such a "go with the winners" scheme in the concrete setting of searching for a deep leaf in a tree. There are two relevant parameters of the tree: its depth d, and another parameter which is a measure of the imbalance of the tree. We prove that the running time of the "go with the winners" scheme (to achieve 99% probability of success) is bounded by a polynomial in d and . By contrast, the simple restart scheme: run several independent

This paper presents two primary results relevant tophysical design problems in CAD/VLSI through a case study of the linear placement problem. First a local search mechanism which incorporates a sophisticated neighborhood operator based on constraint relaxation is proposed. The strategy exhibits many ofthe desirable features of analytical placement while retaining the exibility and non-determinism of local search. The second and orthogonal contribution is in netlist clustering. We characterize local optima in the linear placement problem through a simple visualization tool { the displacement graph. This characterization reveals the relation-ship between clusters and local optima and motivates a dynamic clustering scheme designed speci cally for escaping such local optima. Promising experimental results are reported.

This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater probability of success than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolution strategies and evolutionary programming, genetic programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications for the performance of a variety of other optimization algorithms. 1 Introduction This paper concerns the performance of algorithms that minimize an objective function of the form f : S ! R, jSj ! 1. In particular, this paper concerns the relative performance of evolutionary...

Simulated annealing is a popular and much studied method for maximizing functions on finite or compact spaces. For noncompact state spaces, the method is still sound, but convergence results are scarce. We show here how to prove convergence in such cases, for Markov chains satisfying suitable drift and minorization conditions.