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As the sustained trend towards integrating more and more functionality into systems on a chip can be observed in all fields, their economic realization is a challenge for the chip making industry. This is, however, barely possible today, as the ability to design and verify such complex systems could not keep up with the rapid technological development. Owing to this productivity gap, a design methodology, mainly using pre designed and pre verifying blocks, is mandatory. The availability of such blocks, meeting the highest possible quality standards, is decisive for its success. Cost-effective, this can only be achieved by formal verification on the block-level, namely by checking properties, ranging over finite intervals of time. As this verification approach is based on constructing and solving Boolean equivalence problems, it allows for using backtrack search procedures, such as SAT. Recent improvements of the latter are responsible for its high capacity. Still, the verification of some classes of hardware designs, enjoying regular substructures or complex arithmetic data paths, is difficult and often intractable. For regular designs, this is mainly due to individual treatment of symmetrical parts of the search space by backtrack search procedures used. One approach to tackle these deficiencies, is to exploit the regular structure for problem reduction on the register transfer level (RTL). This work describes a new approach for property checking on the RTL, preserving the problem inherent structure for subsequent reduction. The reduction is based on eliminating symmetrical parts from bitvector functions, and hence, from the search space. Several approaches for symmetry reduction in search problems, based on invariance of a function under permutation of variables, have been previously proposed. Unfortunately, our investigations did not reveal this kind of symmetry in relevant cases. Instead, we propose a reduction based on symmetrical values, as we encounter them much more frequently in our industrial examples. Let \(f\) be a Boolean function. The values \(0\) and \(1\) are symmetrical values for a variable \(x\) in \(f\) iff there is a variable permutation \(\pi\) of the variables of \(f\), fixing \(x\), such that \(f|_{x=0} = \pi(f|_{x=1})\). Then the question whether \(f=1\) holds is independent from this variable, and it can be removed. By iterative application of this approach to all variables of \(f\), they are either all removed, leaving \(f=1\) or \(f=0\) trivially, or there is a variable \(x'\) with no such \(\pi\). The latter leads to the conclusion that \(f=1\) does not hold, as we found a counter-example either with \(x'=0\), or \(x'=1\). Extending this basic idea to vectors of variables, allows to elevate it to the RTL. There, self similarities in the function representation, resulting from the regular structure preserved, can be exploited, and as a consequence, symmetrical bitvector values can be found syntactically. In particular, bitvector term-rewriting techniques, isomorphism procedures for specially manipulated term graphs, and combinations thereof, are proposed. This approach dramatically reduces the computational effort needed for functional verification on the block-level and, in particular, for the important problem class of regular designs. It allows the verification of industrial designs previously intractable. The main contributions of this work are in providing a framework for dealing with bitvector functions algebraically, a concise description of bounded model checking on the register transfer level, as well as new reduction techniques and new approaches for finding and exploiting symmetrical values in bitvector functions.

This work presents a new framework for Gröbner basis computations with Boolean polynomials. Boolean polynomials can be modeled in a rather simple way, with both coefficients and degree per variable lying in {0, 1}. The ring of Boolean polynomials is, however, not a polynomial ring, but rather the quotient ring of the polynomial ring over the field with two elements modulo the field equations x2 = x for each variable x. Therefore, the usual polynomial data structures seem not to be appropriate for fast Gröbner basis computations. We introduce a specialized data structure for Boolean polynomials based on zero-suppressed binary decision diagrams (ZDDs), which is capable of handling these polynomials more efficiently with respect to memory consumption and also computational speed. Furthermore, we concentrate on high-level algorithmic aspects, taking into account the new data structures as well as structural properties of Boolean polynomials. For example, a new useless-pair criterion for Gröbner basis computations in Boolean rings is introduced. One of the motivations for our work is the growing importance of formal hardware and software verification based on Boolean expressions, which suffer – besides from the complexity of the problems – from the lack of an adequate treatment of arithmetic components. We are convinced that algebraic methods are more suited and we believe that our preliminary implementation shows that Gröbner bases on specific data structures can be capable to handle problems of industrial size.