**8.1 Memetic approach to circuit partitioning**

Memetic algorithm (MA) are population based heuristic search approaches for combinatorial optimization problems based on cultural evolution. They are designed to search in the space of locally optimal solutions instead of searching in the space of all candidate solutions. This is achieved by applying local search after each of the genetic operators. Crossover and mutation operators are applied to randomly chosen individuals for a predefined number of times. To maintain local optimality, the local search procedure is applied to the newly created individuals.

Algorithms for CAD Tools VLSI Design 161

In recognition stage the parameters, that is, centroid and probability are fed into generalized delta rule algorithm separately and train the network to recognize sub circuit with

A new model for partitioning a circuit is proposed using DBSCAN and fuzzy ARTMAP neural network. The first step is concerned with feature extraction, where it uses DBSCAN algorithm. The second step is classification and is composed of a fuzzy ARTMAP neural

Two clustering algorithms Nearest Neighbor (NNA) and Partitoning Around Medoids (PAM) clustering algorithms are considered for dividing the circuits into sub circuits. Clustering is alternatively referred to as unsupervised learning segmentation. The clusters are formed by finding the similarities between data according to characteristics found in the actual data. NNA is a serial algorithm in which the items are iteratively merged into the

Testing: The algorithms are tested using VHDL,Xilinx xc9500 CPLD/FPGA tool and

As the number of clusters increases, the time taken for PAM increases but is less than Nearest Neighbor algorithm. PAM performs better than Nearest Neighbor algorithm. PAM has been very competent, especially in the case of a large number of cells when compared with Nearest Neighbor. The proposed model based algorithm has achieved sub-circuits with

**8.4 Nearest Neighbor and Partitioning Around Medoids clustering Algorithms** 

existing clusters that are closest. PAM represents a cluster by a medoid.

Criteria Used: Clustering/Unsupervised learning segmentation

minimum interconnections, for the Circuit Partitioning problem.

MATLAB simulator using a test netlist matrix .

Fig. 27. Block diagram of K-means with neural network

Fig. 28. Block diagram of EM methodology with neural network

minimum interconnection between them

**8.3 Fuzzy ARTMAP with DBSCAN** 

Results and Observations:

network.

Neuro-Memetic Approach to Circuit Partitioning makes it possible to predict the sub-circuit from circuit with minimum interconnections between them. The system consists of three parts, each dealing with data extraction, learning stage & result stage. In data extraction, a circuit is bipartite and chromosomes are represented for each sub circuit. Extracted sequences are fed to Neuro-memetic model that would recognize sub-circuits with lowest amount of interconnections between them.

Fig. 26. Working procedure of Neuro-Memetic approach

#### **8.2 Neuro-EM model**

The system consists of three parts each dealing with data extraction, Learning stage and recognition stage. In data extraction, a circuit is bipartite and partitions it into 10 clusters, a user-defined value, by using K-means (J. B. MacQueen, 1967) and EM methodology (Kaban & Girolami, 2000), respectively. In recognition stage the parameters, centroid and probability are fed into generalized delta rule algorithm separately and train the network to recognize sub-circuits with lowest amount of interconnections between them

Neuro-Memetic Approach to Circuit Partitioning makes it possible to predict the sub-circuit from circuit with minimum interconnections between them. The system consists of three parts, each dealing with data extraction, learning stage & result stage. In data extraction, a circuit is bipartite and chromosomes are represented for each sub circuit. Extracted sequences are fed to Neuro-memetic model that would recognize sub-circuits with lowest

amount of interconnections between them.

Fig. 26. Working procedure of Neuro-Memetic approach

The system consists of three parts each dealing with data extraction, Learning stage and recognition stage. In data extraction, a circuit is bipartite and partitions it into 10 clusters, a user-defined value, by using K-means (J. B. MacQueen, 1967) and EM methodology (Kaban & Girolami, 2000), respectively. In recognition stage the parameters, centroid and probability are fed into generalized delta rule algorithm separately and train the network to

recognize sub-circuits with lowest amount of interconnections between them

**8.2 Neuro-EM model** 

Fig. 27. Block diagram of K-means with neural network

Fig. 28. Block diagram of EM methodology with neural network

In recognition stage the parameters, that is, centroid and probability are fed into generalized delta rule algorithm separately and train the network to recognize sub circuit with minimum interconnection between them
