Near optimal machine learning based random test generation

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.