scholarly journals A Degree-Based Clustering Technique for VLSI Placement

2009 ◽  
Vol 3 (3) ◽  
pp. 425-441
Author(s):  
Jie Huang ◽  
Laleh Behjat ◽  
Logan Rakai ◽  
Jianhua Li
Keyword(s):  
Clustering Algorithm ◽  
Wire Length ◽  
Placement Problem ◽  
Clustering Technique ◽  
Length Data ◽  
Circuit Placement ◽  
High Degree

In this paper, clustering for the circuit placement problem is examined from the perspective of wire length contribution from groups of nets. First, the final wire length data of groups of nets with different degrees are extracted and studied. It is illustrated that nets with high-degree contribute a high percentage to the total wire length. To remedy this problem, a clustering algorithm for placement is proposed that focuses on clustering nets with high-degree. This new clustering algorithm is implemented as a preprocessing step in the placement stage. ICCAD04 benchmark circuits abstracted from IBM are used to validate the placement quality by using four academic placers with and without the proposed preprocessing step. Experiments show that the overall placement results can be improved by up to 5%.

scholarly journals A New Length-Based Algebraic Multigrid Clustering Algorithm

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
L. Rakai ◽  
A. Farshidi ◽  
L. Behjat ◽  
D. Westwick
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Clustering Algorithms ◽  
Algebraic Multigrid ◽  
Estimation Technique ◽  
Wire Length ◽  
Clustering Technique ◽  
Length Estimation ◽  
Made In

Clustering algorithms have been used to improve the speed and quality of placement. Traditionally, clustering focuses on the local connections between cells. In this paper, a new clustering algorithm that is based on the estimated lengths of circuit interconnects and the connectivity is proposed. In the proposed algorithm, first an a priori length estimation technique is used to estimate the lengths of nets. Then, the estimated lengths are used in a clustering framework to modify a clustering technique based on algebraic multigrid (AMG), that finds the cells with the highest connectivity. Finally, based on the results from the AMG-based process, clusters are made. In addition, a new physical unclustering technique is proposed. The results show a significant improvement, reductions of up to 40%, in wire length can be achieved when using the proposed technique with three academic placers on industry-based circuits. Moreover, the runtime is not significantly degraded and can even be improved.

scholarly journals Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

2019 ◽  
Vol 9 (2S) ◽  
pp. 9-13
Keyword(s):  
Cluster Analysis ◽  
Precision Agriculture ◽  
Crop Production ◽  
Clustering Algorithm ◽  
Research Work ◽  
K Value ◽  
Clustering Technique ◽  
Production Analysis ◽  
Initial Cluster ◽  
The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

Research on K-Anonymity Algorithm in Privacy Protection

2013 ◽  
Vol 756-759 ◽  
pp. 3471-3475 ◽  
Author(s):  
Chen Wang ◽  
Lian Zhong Liu ◽  
Li Jie Gao
Keyword(s):  
Privacy Protection ◽  
Clustering Algorithm ◽  
Information Loss ◽  
High Degree

Nowadays, people pay great attention to the privacy protection, therefore the technology of anonymization has been widely used. However, most of current methods strictly depend on the predefined ordering relation on the generalization layer or attribute domain, making the anonymous result is a high degree of information loss, thereby reducing the availability of data. In order to solve the problem, we propose a K-Members Clustering Algorithm to reduce the information loss, and improve the performance of k-anonymity in privacy protection.

scholarly journals Innovativeness of Senior Consumers’ Attitudes – An Attempt to Conduct Segmentation

2021 ◽  
Vol 21 (1) ◽  
pp. 76-91
Author(s):  
Tomasz Olejniczak
Keyword(s):  
Clustering Algorithm ◽  
The Elderly ◽  
Social Variables ◽  
Consumer Market ◽  
Product Innovations ◽  
The Press ◽  
Research Procedure ◽  
The Media ◽  
High Degree ◽  
Selection Of

Abstract Research background: Ageing societies is a global phenomenon. In the media and the press, there strongly exists a stereotype of elderly people being poorly adjusted to social change, lonely, poor and not open to market novelties. However, an in-depth description of the senior consumer market segment shows their high degree of variety and differentiation in their approach to purchasing product innovations. Purpose: The aim of the article is to present a model of segmentation of older people according to the criterion of their attitudes towards innovation on the FMCG market. Research methodology: The source of information for building the typology was personal interviews conducted in 2018 with people aged over 60 in Greater Poland (the Wielkopolska region). The selection of respondents (the sample consisted of 1,451 people) was carried out using the quota selection method. The K-means clustering algorithm was applied to classify the respondents into clusters. Results: The research procedure allowed for the identification of different segments of senior consumers: reluctant conservatives, knowledgeable traditionalists, aspiring withdrawn/passive, unaware practical and rational innovators. Novelty: The results of the analysis of the segmentation of senior consumers confirm that people actively interested in product innovations on the FMCG market constitute a relatively small part of the elderly population (18.0%) and that there is a clear relationship between the attitude towards innovation and the characteristics of consumers in terms of economic, demographic and social variables.

scholarly journals A Fast Clustering-Based Min-Cut Placement Algorithm With Simulated-Annealing Performance

VLSI Design ◽  
1996 ◽  
Vol 5 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Youssef Saab
Keyword(s):  
Simulated Annealing ◽  
Integrated Circuits ◽  
Large Scale ◽  
Optimization Problem ◽  
Wire Length ◽  
Constrained Optimization Problem ◽  
Placement Problem ◽  
Constructive Methods ◽  
Placement Algorithm ◽  
Min Cut

Placement is an important constrained optimization problem in the design of very large scale (VLSI) integrated circuits [1–4]. Simulated annealing [5] and min-cut placement [6] are two of the most successful approaches to the placement problem. Min-cut methods yield less congested and more routable placements at the expense of more wire-length, while simulated annealing methods tend to optimize more the total wire-length with little emphasis on the minimization of congestion. It is also well known that min-cut algorithms are substantially faster than simulated-annealing-based methods. In this paper, a fast min-cut algorithm (ROW-PLACE) for row-based placement is presented and is empirically shown to achieve simulated-annealing-quality wire-length on a number of benchmark circuits. In comparison with Timberwolf 6 [7], ROW-PLACE is at least 12 times faster in its normal mode and is at least 25 times faster in its faster mode. The good results of ROW-PLACE are achieved using a very effective clustering-based partitioning algorithm in combination with constructive methods that reduce the wire-length of nets involved in terminal propagation.

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

A PLACEMENT FLOW FOR VERY LARGE-SCALE MIXED-SIZE CIRCUIT PLACEMENT

2014 ◽  
Vol 23 (02) ◽  
pp. 1450016
Author(s):  
JIANLI CHEN ◽  
WENXING ZHU
Keyword(s):  
Integrated Circuit ◽  
Large Scale ◽  
Main Idea ◽  
Placement Problem ◽  
Circuit Element ◽  
Solution Quality ◽  
Macro Cell ◽  
Circuit Elements ◽  
Cell Placement ◽  
Circuit Placement

The very large-scale integrated circuit (VLSI) placement problem is to determine the exact location of each movable circuit element within a given region. It is a crucial process in physical design, since it affects performance, power consumption, routability, and heat distribution of a design. In this paper, we propose a VLSI placement flow to handle the large-scale mixed-size placement problem. The main idea of our placement flow is using a floorplanning algorithm to guide the placement of circuit elements. It consists of four steps: (1) With the multilevel framework, circuit elements are clustered into blocks by recursively partitioning; (2) a floorplanning algorithm is performed on every level of the blocks; (3) the macro cells are shifted by a macro shifting technique to determine their exact locations; (4) with each macro cell location fixed, a standard cell placement algorithm is applied to place the remaining objects. The proposed approach is tested on the IBM mixed-size benchmarks and the modern mixed-size (MMS) placement benchmarks. Experimental results show that our approach outperforms the state-of-the-art placers on the solution quality for most of the benchmarks.

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