A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits
| Autor: | Abdolreza Mirzaei, Behzad Moradi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science 020208 electrical & electronic engineering Decision tree Evolutionary algorithm Process (computing) 02 engineering and technology Machine learning computer.software_genre 020202 computer hardware & architecture law.invention Reduction (complexity) CMOS law 0202 electrical engineering electronic engineering information engineering Operational amplifier Electronic engineering Artificial intelligence Electrical and Electronic Engineering business Engineering design process computer Learnable Evolution Model |
| Zdroj: | International Journal of Electronics. 103:1868-1881 |
| ISSN: | 1362-3060 0020-7217 |
| DOI: | 10.1080/00207217.2016.1138538 |
| Popis: | A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer’s knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order ... |
| Databáze: | OpenAIRE |
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