Verification, as opposed to Testing and Post-Silicon Validation, is a critical step for Integrated Circuits (IC) Design, answering the question "Are we designing the right function?" before the chips are manufactured. One of the core areas of Verification is Equivalence Checking (EC), which is a special yet independent case of Model Checking (MC). Equivalence Checking aims to prove that two circuits, when fed with the same inputs, produce the exact same outputs. There are broadly two ways to conduct Equivalence Checking, simulation and Formal Equivalence Checking. Simulation requires one to try out different input combinations and observe if the two circuits produce the same output. Obviously, since it is not possible to enumerate all combinations of different inputs, completeness cannot be guaranteed. On the other hand, Formal Equivalence Checking can achieve 100% confidence. As the number of gates and in particular, the number of flip-flops, in circuits has grown tremendously during the recent years, the problem of Formal Equivalence Checking has become much harder â A recent evaluation of a general-case Formal Equivalence Checking engine [1] shows that about 15% of industrial designs cannot be verified after a typical sequential synthesis flow. As a result, a lot of attention on Formal Equivalence Checking has been drawn both academically and industrially. For years Combinational Equivalence Checking(CEC) has been the pervasive framework for Formal Equivalence Checking(FEC) in the industry. However, due to the limitation of being able to verify circuits only with 1:1 flip-flop pairing, a pure CEC-based methodology requires a full regression of the verification process, meaning that performing sequential optimizations like retiming or FSM re-encoding becomes somewhat of a bottleneck in the design cycle [2]. Therefore, a more powerful framework — Sequential Equivalence Checking (SEC) — has been gradually adopted in industry. In this thesis, we target on Sequential Equivalence Checking by finding efficient yet powerful group of relationships (invariants) among the signals of the two circuits being compared. In order to achieve a high success rate on some of the extremely hard-to-verify circuits, we are interested in both two-node and multi-node (up to 4 nodes) invariants. Also we are interested in invariants among both flip-flops and internal signals. For large circuits, there can be too many potential invariants requiring much time to prove. However, we observed that a large portion of them may not even contribute to equivalence checking. Moreover, equivalence checking can be significantly helped if there exists a method to check if a subset of potential invariants would be sufficient (e.g., whether two-nodes are enough or multi-nodes are also needed) prior to the verification step. Therefore, we propose two sufficiency-based approaches to identify useful invariants out of the initial potential invariants for SEC. Experimental results show that our approach can either demonstrate insufficiency of the invariants or select a small portion of them to successfully prove the equivalence property. Our approaches are quite case-independent and flexible. They can be applied on circuits with different synthesis techniques and combined with other techniques. / Master of Science

With the advances in VLSI and System-On-Chip (SOC) technology, the complexity of hardware systems has increased manifold. Today, 70% of the design cost is spent in verifying these intricate systems. The two most widely used formal methods for design verification are Equivalence Checking and Model Checking. Equivalence Checking requires that the implementation circuit should be exactly equivalent to the specification circuit (golden model). In other words, for each possible input pattern, the implementation circuit should yield the same outputs as the specification circuit. Model checking, on the other hand, checks to see if the design holds certain properties, which in turn are indispensable for the proper functionality of the design. Complexities in both Equivalence Checking and Model Checking are exponential to the circuit size. In this thesis, we firstly propose a novel technique to improve SAT-based Combinational Equivalence Checking (CEC) and Bounded Model Checking (BMC). The idea is to perform a low-cost preprocessing that will statically induce global signal relationships into the original CNF formula of the circuit under verification and hence reduce the complexity of the SAT instance. This efficient and effective preprocessing quickly builds up the implication graph for the circuit under verification, yielding a large set of logic implications composed of direct, indirect and extended backward implications. These two-node implications (spanning time-frame boundaries) are converted into two-literal clauses, and added to the original CNF database. The added clauses constrain the search space of the SAT-solver engine, and provide correlation among the different variables, which enhances the Boolean Constraint Propagation (BCP). Experimental results on large and difficult ISCAS'85, ISCAS'89 (full scan) and ITC'99 (full scan) CEC instances and ISCAS'89 BMC instances show that our approach is independent of the state-of-the-art SAT-solver used, and that the added clauses help to achieve more than an order of magnitude speedup over the conventional approach. Also, comparison with Hyper-Resolution [Bacchus 03] suggests that our technique is much more powerful, yielding non-trivial clauses that significantly simplify the SAT instance complexity. Secondly, we propose a novel global learning technique that helps to identify highly non-trivial relationships among signals in the circuit netlist, thereby boosting the power of the existing implication engine. We call this new class of implications as 'extended forward implications', and show its effectiveness through additional untestable faults they help to identify. Thirdly, we propose a suite of lemmas and theorems to formalize global learning. We show through implementation that these theorems help to significantly simplify a generic CNF formula (from Formal Verification, Artificial Intelligence etc.) by identifying the necessary assignments, equivalent signals, complementary signals and other non-trivial implication relationships among its variables. We further illustrate through experimental results that the CNF formula simplification obtained using our tool outshines the simplification obtained using other preprocessors. / Master of Science

Powerful sequential optimization techniques can drastically change the Integrated Circuit (IC) design paradigm. Due to the limited capability of sequential verification tools, aggressive sequential optimization is shunned nowadays as there is no efficient way to prove the preservation of equivalence after optimization. Due to the fact that the number of transistors fitting on single fixed-size die increases with Moore's law, the problem gets harder over time and in an exponential rate. It is no surprise that functional verification becomes a major bottleneck in the time-to-market of a product. In fact, literature has reported that 70% of design time is spent on making sure the design is bug-free and operating correctly. One of the core verification tasks in achieving high quality products is equivalence checking. Essentially, equivalence checking ensures the preservation of optimized product's functionality to the unoptimized model. This is important for industry because the products are modified constantly to meet different goals such as low power, high performance, etc. The mainstream in conducting equivalence checking includes simulation and formal verification. In simulation approach, golden design and design under verification (DUV) are fed with same stimuli for input expecting outputs to produce identical responses. In case of discrepancy, traces will be generated and DUV will undergo modifications. With the increase in input pins and state elements in designs, exhaustive simulation becomes infeasible. Hence, the completeness of the approach is not guaranteed and notions of coverage has to be accompanied. On the other hand, formal verification incorporates mathematical proofs and guarantee the completeness over the search space. However, formal verification has problems of its own in which it is usually resource intensive. In addition, not all design can be verified after optimization processes. That is to say the golden model and DUV are vastly different in structure which cause modern checker to give inconclusive result. Due to this nature, this thesis focuses in improving the strength and the efficiency of sequential equivalence checking (SEC) using formal approach. While there has been great strides made in the verification for combinational circuits, SEC still remains rather rudimentary. Without powerful SEC as a backbone, aggressive sequential synthesis and optimization are often avoided if the optimized design cannot be proved to be equivalent to the original one. In an attempt to take on the challenges of SEC, we propose two frameworks that successfully determining equivalence for hard-to-verify circuits. The first framework utilizes arbitrary relations between any two nodes within the two sequential circuits in question. The two nodes can reside in the same or across the circuits; likewise, they can be from the same time-frame or across time-frames. The merit for this approach is to use global structure of the circuits to speed up the verification process. The second framework introduces techniques to identify subset but yet powerful multi-node relations (involve more than 2 nodes) which then help to prune large don't care search space and result in a successful SEC framework. In contrast with previous approaches in which exponential number of multi-node relations are mined and learned, we alleviate the computation cost by selecting much fewer invariants to achieve desired conclusion. Although independent, the two frameworks could be used in sequential to complement each other. Experimental results demonstrate that our frameworks can take on many hard-to-verify cases and show a significant speed up over previous approaches. / Master of Science