Test scheduling of System-on-Chip using Dragonfly and Ant Lion optimization algorithms

2020 ◽  
pp. 1-13
Author(s):  
Gokul Chandrasekaran ◽  
P.R. Karthikeyan ◽  
Neelam Sanjeev Kumar ◽  
Vanchinathan Kumarasamy
Keyword(s):  
Ant Colony Optimization ◽  
Artificial Bee Colony ◽  
Optimization Algorithms ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
On Chip

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

Minimization of test time in system on chip using artificial intelligence-based test scheduling techniques

2019 ◽  
Vol 32 (9) ◽  
pp. 5303-5312 ◽  
Author(s):  
Gokul Chandrasekaran ◽  
Sakthivel Periyasamy ◽  
Karthikeyan Panjappagounder Rajamanickam
Keyword(s):  
Artificial Intelligence ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
On Chip

Test Scheduling Method Based on Cellular Genetic Algorithm for System on Chip

2010 ◽  
Vol 663-665 ◽  
pp. 670-673
Author(s):  
Zhong Liang Pan ◽  
Ling Chen
Keyword(s):  
Genetic Algorithm ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Scheduling Problem ◽  
Good Test ◽  
Cellular Genetic Algorithm ◽  
Scheduling Method ◽  
On Chip ◽  
Feasible Solutions

The main aspects for the test of system on chip (SoC) are designing testability architectures and solving the test scheduling. The test time of SoC can be reduced by using good test scheduling schemes. A test scheduling method based on cellular genetic algorithm is presented in this paper. In the method, the individuals are used to represent the feasible solutions of the test scheduling problem, the individuals are distributed over a grid or connected graph, the genetic operations such as selection and mutation are applied locally in some neighborhood of each individual. The test scheduling schemes are obtained by carrying out the evolutionary operations for the populations. A lot of experiments are performed for the SoC benchmark circuits, the experimental results show that the better test scheduling schemes can be obtained by the method in this paper.

scholarly journals Test Scheduling of Core Based System-on-Chip Using Modified Ant Colony Optimization

2019 ◽  
Vol 52 (6) ◽  
pp. 599-605 ◽  
Author(s):  
Gokul Chandrasekaran ◽  
Vanchinathan Kumarasamy ◽  
Gnanavel Chinraj
Keyword(s):  
Ant Colony Optimization ◽  
System On Chip ◽  
Ant Colony ◽  
Test Scheduling ◽  
On Chip

scholarly journals An improved form of the ant lion optimization algorithm for image clustering problems

2019 ◽  
pp. 1445-1460 ◽  
Author(s):  
METİN TOZ
Keyword(s):  
Objective Function ◽  
Artificial Bee Colony ◽  
Image Clustering ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Objective Function Values ◽  
Lion Optimization Algorithm ◽  
Clustering Problems

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.

scholarly journals Measuring Static Parameters of Embedded ADC Core

2011 ◽  
Vol 62 (2) ◽  
pp. 80-86
Author(s):  
Franc Novak ◽  
Peter Mrak ◽  
Anton Biasizzo
Keyword(s):  
Feasibility Study ◽  
Theoretical Background ◽  
System On Chip ◽  
Test Time ◽  
Hardware Overhead ◽  
Self Test ◽  
Built In Self Test ◽  
On Chip ◽  
The Impact ◽  
Minimal Hardware

Measuring Static Parameters of Embedded ADC CoreThe paper presents the results of a feasibility study of measuring static parameters of ADC cores embedded in a System-on-Chip. Histogram based technique is employed because it is suitable for built-in self-test. While the theoretical background of the technique has been covered by numerous papers, less attention has been given to implementations in practice. Our goal was the implementation of histogram test in a IEEE Std 1500 wrapper. Two different solutions pursuing either minimal test time or minimal hardware overhead are described. The impact of MOS switches at ADC input on the performed measurements was considered.

CMCABC: Clustering and Memory-Based Chaotic Artificial Bee Colony Dynamic Optimization Algorithm

2018 ◽  
Vol 17 (04) ◽  
pp. 1007-1046 ◽  
Author(s):  
Mohsen Moradi ◽  
Samad Nejatian ◽  
Hamid Parvin ◽  
Vahideh Rezaie
Keyword(s):  
Dynamic Optimization ◽  
Real World ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Dynamic Environments ◽  
Dynamic Optimization Problems ◽  
The Real ◽  
Bee Colony ◽  
Swarm Intelligence Optimization

The swarm intelligence optimization algorithms are used widely in static purposes and applications. They solve the static optimization problems successfully. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments as well. The dynamic optimization problems indicate the ones whose solutions change over time. The artificial bee colony algorithm is one of the swarm intelligence optimization algorithms. In this study, a clustering and memory-based chaotic artificial bee colony algorithm, denoted by CMCABC, has been proposed for solving the dynamic optimization problems. A chaotic system has a more accurate prediction for future in the real-world applications compared to a random system, because in the real-world chaotic behaviors have emerged, but random behaviors havenot been observed. In the proposed CMCABC method, explicit memory has been used to save the previous good solutions which are not very old. Maintaining diversity in the dynamic environments is one of the fundamental challenges while solving the dynamic optimization problems. Using clustering technique in the proposed method can well maintain the diversity of the problem environment. The proposed CMCABC method has been tested on the moving peaks benchmark (MPB). The MPB is a good simulator to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMCABC method compared to the other state-of-the-art methods in solving dynamic optimization problems.

scholarly journals Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony

2021 ◽  
pp. 104-113
Author(s):  
Г.В. Худов ◽  
І.А. Хижняк
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Artificial Bee Colony ◽  
Visual Assessment ◽  
Comparative Assessment ◽  
Ant Colony ◽  
Bee Colony ◽  
Segmentation Methods ◽  
Segmentation Quality

The article discusses the methods of swarm intelligence, namely, an improved method based on the ant colony optimization and the method of an artificial bee colony. The goal of the work is to carry out a comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony. Segmentation of tonal optical-electronic images was carried out using the proposed methods of swarm intelligence. The results of the segmentation of optical-electronic images obtained from the spacecraft are presented. A visual assessment of the quality of segmentation results was carried out using improved methods. The classical errors of the first and second kind of segmentation of optoelectronic images are calculated for the proposed methods of swarm intelligence and for known segmentation methods. The features of using each of the proposed methods of swarm intelligence are determined. The tasks for which it is better to use each of the proposed methods of swarm intelligence are determined.

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