6722156
An efficient and effective detailed placement algorithm
In the past few years, there has been a lot of research in the area of global placement. In comparison, not much attention has been paid to the detailed placement problem. Existing detailed placers either fail to improve upon the excellent solution quality enabled by good global placers or are very slow. To handle the above problems, we focus on the detailed placement problem. We present an efficient and effective detailed placement algorithm to handle the wirelength minimization problem. The main contributions of our work are: (1) an efficient Global Swap technique to identify a pair of cells that can be swapped to reduce wirelength; (2) a flow that combines the Global Swap technique with other heuristics to produce very good wirelength; (3) an efficient single-segment clustering technique to optimally shift cells within a segment to minimize wirelength. On legalized mPL5 global placements on the IBM standard-cell benchmarks, our detailed placer can achieve 19.0%, 13.2% and 0.5% more wirelength reduction compared to Fengshui5.0, rowironing and Domino respectively. Correspondingly we are 3.6/spl times/ 2.8/spl times/ and 15/spl times/ faster. On the ISPD05 benchmarks (Gi-Joon Nam et al., 2005), we achieve 8.1% and 9.1% more wirelength reduction compared to Fengshui5.0 and rowironing respectively. Correspondingly we are 3.1/spl times/ and 2.3/spl times/ faster.
13097054
RQL: Global Placement via Relaxed Quadratic Spreading and Linearization
This paper describes a simple and effective quadratic placement algorithm called RQL. We show that a good quadratic placement, followed by local wirelength-driven spreading can produce excellent results on large-scale industrial ASIC designs. As opposed to the current top performing academic placers [4,7,11], RQL does not embed a linearization technique within the solver. Instead, it only requires a simpler, pure quadratic objective function in the spirit of [8,10,23]. Experimental results show that RQL outperforms all available academic placers on the ISPD-2005 placement contest benchmarks. In particular, RQL obtains an average wire- length improvement of 2.8%, 3.2%, 5.4%, 8.5%, and 14.6% versus mPL6 [5], NTUPlaceS [7], Kraflwerk [20], APlace2.0 [11], and Capo10.2 [18], respectively. In addition, RQL is three, seven, and ten times faster than mpL6, Capo10.2, and APlace2.0, respectively. On the ISPD-2006 placement contest benchmarks, on average, RQL obtains the best scaled wirelength among all available academic placers.
9232977
Placement constraints in floorplan design
In floorplan design, it is common that a designer will want to control the positions of some modules in the final packing for various purposes like datapath alignment and I/O connection. There are several previous works focusing on some particular kinds of placement constraints. In this paper, we will present a unified method to handle all of them simultaneously, including preplace constraint, range constraint, boundary constraint, alignment, abutment, and clustering, etc., in general, nonslicing floorplans. We have used incremental updates and an interesting idea of reduced graph to improve the runtime of the method. We tested our method using some benchmark data with about 1/8 of the modules having placement constraints and the results are very promising. Good packings with all the constraints satisfied can be obtained efficiently.
41348498
Fast and exact simultaneous gate and wire sizing by Lagrangian relaxation
52810492
Optimal cell flipping in placement and floorplanning
In a placed circuit, there are a lot of movable cells that can be flipped to further reduce the total wirelength, without affecting the original placement solution. We aim at solving this flipping problem optimally. However, solving such a problem optimally is non-trivial given the gigantic sizes of modern circuits. We are able to identify a large portion of cells (about 75%) of which the orientation (flipped or not flipped) can be determined independent of the orientations of all the other cells. We have derived three non-trivial conditions to identify those so called independent cells, strictly solvable cells and conditionally solvable cells. In this way, we can greatly reduce the number of cells whose orientations are dependent on each other. Finally, the cell flipping problem of the remaining dependent cells can be formulated as a mixed integer linear programming (MILP) problem and solved optimally. However, this may still be too slow for extremely large circuits and we have applied two other methods, linear programming (LP) and linear programming followed by mixed integer linear programming (LP+MILP) to solve the problem. Experimental results show that by identifying those independent and solvable cells first and applying the LP+MILP technique, we can solve this flipping problem effectively and obtain results just 0.01% more than the optimal. In addition, we can improve the wirelength and number of overflow tiles by 5% and 9% respectively on the floorplanning benchmarks
776801
Optimizing SOC Test Resources using Dual Sequences
In this paper, we propose a new data structure called dual sequences to represent SOC test schedules. Dual sequences are used together with a simulated annealing based procedure to optimize the SOC test application time and tester resources. The problems we consider are generation of optimal test schedules for SOCs and minimizing tester memory and test channels. Results of experiments conducted on ITC’02 benchmark SOCs show the effectiveness of the proposed method.
41639900
An Evolutionary Fuzzy Multi-objective Approach to Cell Formation
Fuzzy mathematical programming (FMP) has been shown not only providing a better and more flexible way of representing the cell formation (CF) problem of cellular manufacturing, but also improving solution quality and computational efficiency. However, FMP cannot meet the demand of real-world applications because it can only be used to solve small-size problems. In this paper, we propose a heuristic genetic algorithm (HGA) as a viable solution for solving large-scale fuzzy multi-objective CF problems. Heuristic crossover and mutation operators are developed to improve computational efficiency. Our results show that the HGA outperforms the FMP and goal programming (GP) models in terms of clustering results, computational time, and user friendliness.
10869493
A dual-hamiltonian-path-based multicasting strategy for wormhole-routed star graph interconnection networks
Multicast is an important collective communication operation on multicomputer systems, in which the same message is delivered from a source node to an arbitrary number of destination nodes. The star graph interconnection network has been recognized as an attractive alternative to the popular hypercube network. In this paper, we first address a dual-hamiltonian-path-based routing model with two virtual channels based on two hamiltonian paths (HPs) and a network partitioning strategy for wormhole-routed star graph networks. Then, we propose three efficient multicast routing schemes on basis of such a model. All of the three proposed schemes are proved deadlock-free. The first scheme, network-selection-based dual-path routing, selects subnetworks that are constructed either by the first HP or by the second HP for dual-path routing. The second one, optimum dual-path routing, selects subnetworks with optimum routing path for dual-path routing. The third scheme, two-phase optimum dual-path routing, includes two phases, source-to-relay and relay-to-destination. Finally, experimental results are given to show that our proposed three routing schemes outperform the unicast-based, the HP, and the single-HP-based dual-path routing schemes significantly.
27561759
Splice site prediction using support vector machines with a Bayes kernel
One of the most important tasks in correctly annotating genes in higher organisms is to accurately locate the DNA splice sites. Although relatively high accuracy has been achieved by existing methods, most of these prediction methods are computationally extensive. Due to the enormous amount of DNA sequences to be processed, the computational speed is an important issue to consider. In this paper, we present a new machine learning method for predicting DNA splice sites, which first applies a Bayes feature mapping (kernel) to project the data into a new feature space and then uses a linear Support Vector Machine (SVM) as a classifier to recognize the true splice sites. The computation time is linear to the number of sequences tested, while the performance is notably improved compared with the Naive Bayes classifier in terms of classification accuracy, precision, and recall. Our classification results are also comparable to the solution quality obtained by the SVMs with polynomial kernels, while the speed of our proposed method is significantly faster. This is a notable improvement in computational modeling considering the huge amount of DNA sequences to be processed.
17686405
A time-series biclustering algorithm for revealing co-regulated genes
Although existing bicluster algorithms claimed to be able to discover co-regulated genes under a subset of given experiment conditions, they ignore the inherent sequential relationship between crucial time points and thus are not applicable to analyze time-series gene expression data. A simple and effective deletion-based algorithm, using the mean squared residue score as a measure, was developed to bicluster time-series gene expression data. While enforcing a threshold value for the score, the algorithm alternately eliminates genes and time points according to their correlation to the bicluster. To ensure the time locality, only the starting and ending points in the time interval are eligible for deletion. As a result, the number of genes and the length of time interval are simultaneously maximized. Our experimental results shown that the proposed method is capable of identifying co-regulated genes characterized by partial time-course data that previous methods failed to discover.
12743821
Response time behavior of distributed voting algorithms for managing replicated data
Voting is a simple and yet effective way of managing replicated data in distributed systems. In this paper we analyze its response time behavior. We investigate a technique for obtaining the access time distribution for requests that access replicated data maintained by the distributed system. The technique is based on Petri net modeling and can be used to estimate the reliability of real-time applications which must access replicated data with a deadline requirement.