A signal correlation guided ATPG solver and its applications for solving difficult industrial cases

Abstract

The developments of efficient SAT solvers have attracted tremendous research interest in recent years. The merits of these solvers are often compared in terms of their performance based upon a wide spread of benchmarks. In this paper, we extend an earlier-proposed solver design concept called (SCGL) Signal Correlation Guided Learning that is ATPG-based into a family of heuristics. Along with this SCGL family of heuristics, we classify benchmark examples according to their performance using the SCGL heuristics. With this study, we identify the class of problems that are uniquely suitable to be solved by using the SCGL approach. In particular, for solving difficult circuit-based problems at INTEL, our SCGL-based ATPG solver is able to achieve at least an order of magnitude speedup over the state-of-the-art SAT solvers. Our conclusion is that SCGL is an unique solver design concept that can complement heuristics proposed by others for solving circuit-oriented difficult problems.

Reducing Structural Bias in Technology Mapping

“…(The details of this package are in the technical report [18]. A discussion of the issues involved can also be found in recent work on structure-based equivalence checking [10,11,14,15]. )…”

Improvements to Technology Mapping for LUT-Based FPGAs

“…The key idea is to use recent advances in equivalence checking that are based on identifying functionally equivalent internal points in the networks being checked [13] [15].…”

Boosting Verification by Automatic Tuning of Decision Procedures

“…Lu at al. [13] exploited signal correlations to design a number of ATPG-specific techniques for SAT solving. Their technique showed roughly an order of magnitude improvement on a small set of ATPG benchmarks.…”