BDD-based symbolic traversals are the state-of-the-art technaque for reachability analysis of Finite State Machines. They are currently limited to medium-small circuits for two reasons: peak BDD size during image computation and BDD explosion for representing state sets. Startang from these limits, this paper presents an optimized traversal technique particularly orzented to the exact exploration of the state space of large machines.This i s possible thanks to: 1 ) temporary simplification of a Fanite State Machine b y removing some of its state elements, 2) a "divide-and-conquer" approach based on state set decomposition. An eflective use of secondary memory allows us to store relevant portions of BDDs and to regularize access to memory, resulting in less page faults. Experimental results show that this approach is particularly eflective on the larger ISCAS'89 and ISCAS'89-addendum'93 circuits.

This paper presents a solution to the problem of reducing the power dissipated by a digital system containing an intellectual proprietary core processor which repeatedly executes a special-purpose program. The proposed method relies on a novel, application-dependent low-power address bus encoding scheme. The analysis of the execution traces of a given program allows an accurate computation of the correlations that may exist between blocks of bits in consecutive patterns; this information can be successfully exploited to determine an encoding which sensibly reduces the bus transition activity. Experimental results, obtained on a set of special-purpose applications, are very satisfactory; reductions of the bus activity up to 64.8% (41.8% on average) have been achieved over the original address streams. In addition, data concerning the quality and the performance of the automatically synthesized encoding/decoding circuits, as well as the results obtained for a realistic core-based design, indicate the practical usefulness of the proposed power optimization strategy.

Extending the applicability of Teachability analysis t o large and real circuits is a k e y issue. In f a c t they are still limited for the following reasons: peak B D D size during image computation, B D D explosion for representing state sets a n d very high sequential depth. Following the promising trend o f partitioning and problem d ecomposition, we present a new approach based on a disjunctive partitioned transition relation and on a n improved iterative squaring. In this approach a Finite State Machine is decomposed and traversed one 'ifunctioning-mode" at a tame b y means of the "disjunc-live" partitioned approach. The overall algorithm aims at lowering the intermediate peak BDD size pushing further Teachability analysis. Experiments on a few industrial circuits containing counters and on s o m e large benchmarks show the feasibility of the approach.

Binary Decision Diagrams (BDDs) have been widely used in synthesis and verification. Boolean Satisfiability (SAT) Solvers, on the other hand, have been gaining ground only recently, with the introduction of efficient implementation procedures. Specifically, while BDDs have been mainly adopted to formally verify the correctness of hardware devices, SAT-based Bounded Model Checking (BMC) has been widely used for debugging.In this paper, we combine BDD and SAT-based methods to increase the efficiency of BMC. We first exploit affordable BDD-based symbolic approximate reachability analysis to gather information on the state space. Then, we use the collected overestimated reachable state sets to restrict the search space of a SAT-based BMC. This is possible by feeding the SAT solver with a description that is the combination of the original BMC problem with the extra information coming from BDD-based symbolic analysis. We develop specific strategies to appropriately mix BDD and SAT efforts, and to efficiently convert BDD-based symbolic state set representations into SAT-oriented ones.Experimental results prove the validity of our strategy to reduce the amount of variable assignments and variable conflicts generated by SAT solvers, with a subsequent significant performance gain. We gather results with four among the most used SAT solvers, namely Chaff, Limmat, BerkMin, and Siege. We could reduce the number of conflicts up to more than 100×, and the verification time up to 30×.

This paper addresses SAT-based Unbounded Model Checking based on Craig Interpolants. This recently introduced methodology is often able to outperform BDDs and other SAT-based techniques on large verification instances. Based on refutation proofs generated by SAT solvers, interpolants provide compact circuit representations of state sets, and abstract away several details non relevant for proofs. We propose three main contributions, aimed at controlling interpolant size and traversal depth. First of all, we introduce interpolant-based dynamic abstraction to reduce the support of the computed interpolant. Second, we propose new advances in interpolant compaction by redundancy removal. Both techniques rely on an effective application of the incremental SAT paradigm. Finally, we also introduce interpolant computation exploiting circuit quantification, instead of SAT refutation proofs. Experimental results are specifically oriented to prove properties, rather than disproving them (bug hunting). They show how the methodology is able to extend the applicability of interpolant based Model Checking to larger and deeper verification instances.