Abstract. ABC is a public-domain system for logic synthesis and formal verification of binary logic circuits appearing in synchronous hardware designs. ABC combines scalable logic transformations based on And-Inverter Graphs (AIGs), with a variety of innovative algorithms. A focus on the synergy of sequential synthesis and sequential verification leads to improvements in both domains. This paper introduces ABC, motivates its development, and illustrates its use in formal verification.

This paper presents a technique for preprocessing combinational logic before technology mapping. The technique is based on the representation of combinational logic using And-Inverter Graphs (AIGs), a networks of two-input ANDs and inverters. The optimization works by alternating DAG-aware AIG rewriting, which reduces area by sharing common logic without increasing delay, and algebraic AIG balancing, which minimizes delay without increasing area. The new technology-independent flow is implemented in a public-domain tool ABC. Experiments on large industrial benchmarks show that the proposed methodology scales to very large designs and is several orders of magnitude faster than SIS and MVSIS while offering comparable or better quality when measured by the quality of the network after mapping.

No abstract

We describe an optimization method for combinational and sequential logic networks, with emphasis on scalability and the scope of optimization. The proposed resynthesis (a) is capable of substantial logic restructuring, (b) is customizable to solve a variety of optimization tasks, and (c) has reasonable runtime on industrial designs. The approach uses don't cares computed for a window surrounding a node and can take into account external don't cares (e.g. unreachable states). It uses a SAT solver and interpolation to find a new representation for a node. This representation can be in terms of inputs from other nodes in the window thus effecting Boolean re-substitution. Experimental results on 6-input LUT networks after high effort synthesis show substantial reductions in area and delay. When applied to 20 large academic benchmarks, the LUT count and logic level is reduced by 45.0% and 12.2%, respectively. The longest runtime for synthesis and mapping is about two minutes. When applied to a set of 14 industrial benchmarks ranging up to 83K 6-LUTs, the LUT count and logic level is reduced by 11.8% and 16.5%, respectively. Experimental results on 6-input LUT networks after high-effort synthesis show substantial reductions in area and delay. The longest runtime is about 30 minutes.

The paper explores several ways to improve the speed and capacity of combinational equivalence checking based on Boolean satisfiability (SAT).

The paper presents several improvements to state-of-theart

This paper describes an improved approach to Boolean network optimization using internal don't-cares. The improvements concern the type of don't-cares computed, their scope, and the computation method. Instead of the traditionally used compatible observability don't-cares (CODCs), we introduce and justify the use of complete don't-cares (CDC). To ensure the robustness of the don't-care computation for very large industrial networks, a optional windowing scheme is implemented that computes substantial subsets of the CDCs in reasonable time. Finally, we give a SAT-based don'tcare computation algorithm that is more efficient than BDD-based algorithms. Experimental results confirm that these improvements work well in practice. Complete don't-cares allow for a reduction in the number of literals compared to the CODCs. Windowing guarantees robustness, even for very large benchmarks on which previous methods could not be applied. SAT reduces the runtime and enhances robustness, making don't-cares affordable for a variety of other Boolean methods applied to the network.

Abstract-Simulation and Boolean satisfiability (SAT) checking are common techniques used in logic verification. This paper shows how simulation and satisfiability (S&S) can be tightly integrated to efficiently compute flexibilities in a multilevel Boolean network, including the following: 1) complete "don't cares" (CDCs); 2) sets of pairs of functions to be distinguished (SPFDs); and 3) sets of candidate nodes for resubstitution. These flexibilities can be used in network optimization to change the network structure while preserving its functionality. In the first two applications, simulation quickly enumerates most of the solutions while SAT detects the remaining solutions. In the last application, simulation efficiently filters out most of the infeasible solutions while SAT checks the remaining candidates. The experimental results confirm that the combination of simulation and SAT offers a computation engine that outperforms binary decision diagrams, which are traditionally used in such applications.