Considering testability at behavioral level: use of transformations for partial scan cost minimization under timing and area constraints

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. The proposed approach has three components: a library of relevant transformation mechanisms, an objective function, and an optimization algorithm. The most effective transformations for testability optimization are identified by analyzing the fundamental relationship between transformational mechanisms and topological and functional properties of the computations that affect testability. A dynamic, two-stage objective function that estimates the area and testability of the final implementation, and also captures enabling and disabling 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 benchmark examples demonstrates significant simultaneous improvement in both area and testability of the final implementations