Recursive learning: a new implication technique for efficient solutions to CAD problems-test, verification, and optimization

Abstract
Motivated by the problem of test pattern generation in digital circuits, this paper presents a novel technique called recursive learning that is able to perform a logic analysis on digital circuits. By recursively calling certain learning functions, it is possible to extract all logic dependencies between signals in a circuit and to perform precise implications for a given set of value assignments. This is of fundamental importance because it represents a new solution to the Boolean satisfiability problem. Thus, what we present is a new and uniform conceptual framework for a wide range of CAD problems including, but not limited to, test pattern generation, design verification, as well as logic optimization problems. Previous test generators for combinational and sequential circuits use a decision tree to systematically explore the search space when trying to generate a test vector. Recursive learning represents an attractive alternative. Using recursive learning with sufficient depth of recursion during the test generation process guarantees that implications are performed precisely; i.e., all necessary assignments for fault detection are identified at every stage of the algorithm so that no backtracks can occur. Consequently, no decision tree is needed to guarantee the completeness of the test generation algorithm. Recursive learning is not restricted to a particular logic alphabet and can be combined with most test generators for combinational and sequential circuits. Experimental results that demonstrate the efficiency of recursive learning are compared with the conventional branch-and-bound technique for test generation in combinational circuits. In particular, redundancy identification by recursive learning is demonstrated to be much more efficient than by previously reported techniques. In an important recent development, recursive learning has been shown to provide significant progress in design verification problems. Also importantly, recursive learning-based techniques have already been shown to be useful for logic optimization. Specifically, techniques based on recursive learning have already yielded better optimized circuits than the well known MIS-II.<>

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