Abstract:
Optimized test generation techniques are required to overcome the ever increasing test cost of digital systems. In this work a near optimal machine learning based approac...Show MoreMetadata
Abstract:
Optimized test generation techniques are required to overcome the ever increasing test cost of digital systems. In this work a near optimal machine learning based approach is proposed to improve the random test generation techniques. The improvements of the proposed method over previous works are exercised in an HDL environment and results for ISCAS benchmarks are reported.
Published in: 2010 East-West Design & Test Symposium (EWDTS)
Date of Conference: 17-20 September 2010
Date Added to IEEE Xplore: 05 April 2011
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Machine Learning ,
- Random Generation ,
- Bootstrap Resampling ,
- Optimal Learning ,
- Random Test Generation ,
- Cost Of Testing ,
- Intelligence ,
- Learning Algorithms ,
- Monte Carlo Simulation ,
- High Coverage ,
- Results Of This Work ,
- Random Vector ,
- Nodes In The Graph ,
- Learning Phase ,
- Weak Connections ,
- Digital Circuits ,
- Example Of Graph ,
- Fault Simulation
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Machine Learning ,
- Random Generation ,
- Bootstrap Resampling ,
- Optimal Learning ,
- Random Test Generation ,
- Cost Of Testing ,
- Intelligence ,
- Learning Algorithms ,
- Monte Carlo Simulation ,
- High Coverage ,
- Results Of This Work ,
- Random Vector ,
- Nodes In The Graph ,
- Learning Phase ,
- Weak Connections ,
- Digital Circuits ,
- Example Of Graph ,
- Fault Simulation
