Application of simple genetic algorithms to sequential circuit test generation

2002 ◽  
Author(s):  
E.M. Rudnick ◽  
J.G. Holm ◽  
D.G. Saab ◽  
J.H. Patel
Keyword(s):  
Genetic Algorithms ◽  
Test Generation ◽  
Sequential Circuit ◽  
Simple Genetic Algorithms

Low Power and High Speed Sequential Circuits Test Architecture

2019 ◽  
Vol 12 ◽  
Author(s):  
Ahmed K. Jameil ◽  
Yasir Amer Abbas ◽  
Saad Al-Azawi
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Test Generation ◽  
High Speed ◽  
Fir Filter ◽  
Sequential Circuits ◽  
Fir Filters ◽  
Design Verification ◽  
Sequential Circuit ◽  
Generation Algorithm

Background: The designed circuits are tested for faults detection in fabrication to determine which devices are defective. The design verification is performed to ensure that the circuit performs the required functions after manufacturing. Design verification is regarded as a test form in both sequential and combinational circuits. The analysis of sequential circuits test is more difficult than in the combinational circuit test. However, algorithms can be used to test any type of sequential circuit regardless of its composition. An important sequential circuit is the finite impulse response (FIR) filters that are widely used in digital signal processing applications. Objective: This paper presented a new design under test (DUT) algorithm for 4-and 8-tap FIR filters. Also, the FIR filter and the proposed DUT algorithm is implemented using field programmable gate arrays (FPGA). Method: The proposed test generation algorithm is implemented in VHDL using Xilinx ISE V14.5 design suite and verified by simulation. The test generation algorithm used FIR filtering redundant faults to obtain a set of target faults for DUT. The fault simulation is used in DUT to assess the benefit of test pattern in fault coverage. Results: The proposed technique provides average reductions of 20 % and 38.8 % in time delay with 57.39 % and 75 % reductions in power consumption and 28.89 % and 28.89 % slices reductions for 4- and 8-tap FIR filter, respectively compared to similar techniques. Conclusions: The results of implementation proved that a high speed and low power consumption design can be achieved. Further, the speed of the proposed architecture is faster than that of existing techniques.

GA-Based Multi-Objective Fuzzy Matter-Element Optimization

2002 ◽  
Author(s):  
Yanwei Zhao ◽  
Ertian Hua ◽  
Guoxian Zhang ◽  
Fangshun Jin
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problem ◽  
Fuzzy Optimization ◽  
Optimization Methods ◽  
Optimization Criterion ◽  
Multi Objective ◽  
Simple Genetic Algorithms ◽  
Solving Strategy ◽  
Crossover And Mutation ◽  
Better Than

The solving strategy of GA-Based Multi-objective Fuzzy Matter-Element optimization is put forward in this paper to the kind of characters of product optimization such as multi-objective, fuzzy nature, indeterminacy, etc. Firstly, the model of multi-objective fuzzy matter-element optimization is created in this paper, and then it defines the matter-element weightily and changes solving multi-objective optimization into solving dependent function K(x) of the single objective optimization according to the optimization criterion. In addition, modified adaptive macro genetic algorithms (MAMGA) are adopted to solve the optimization problem. It emphatically modifies crossover and mutation operator. By the comparing MAMGA with adaptive macro genetic algorithms (AMGA), not only the optimization is a little better than the latter, but also it reaches the extent to which the effective iteration generation is 62.2% of simple genetic algorithms (SGA). Lastly, three optimization methods, namely fuzzy matter-element optimization, linearity weighted method and fuzzy optimization, are also compared. It certifies that this method is feasible and valid.

scholarly journals An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

2015 ◽  
Vol 66 (4) ◽  
pp. 185-193 ◽  
Author(s):  
Ján Hudec ◽  
Elena Gramatová
Keyword(s):  
Genetic Algorithms ◽  
Test Generation ◽  
Functional Test ◽  
Evolutionary Strategies ◽  
Instruction Set ◽  
Functional Tests ◽  
Automatic Test Generation ◽  
Code Coverage ◽  
Fitness Functions

Abstract The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

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