Synthesis-for-testability using transformations

We address the problem of transforming a behavioral specification so that synthesis of a testable implementation from the new specification requires significantly less area and partial scan cost than synthesis from the original specification. A two-stage objective function, that estimates the area and testability of the final implementation, and also captures enabling effects of the transformations, is developed. Optimization is done using a new randomized branch and bound steepest descent algorithm. Application of the transformation algorithm on several examples demonstrates significant simultaneous improvement in both area and testability of the final implementations.