Conor Madigan

Chaff: engineering an efficient SAT solver

Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

Efficient conflict driven learning in a boolean satisfiability solver

One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.

Sensitivity gains in chemosensing by lasing action in organic polymers

Societal needs for greater security require dramatic improvements in the sensitivity of chemical and biological sensors. To meet this challenge, increasing emphasis in analytical science has been directed towards materials and devices having highly nonlinear characteristics; semiconducting organic polymers (SOPs), with their facile excited state (exciton) transport, are prime examples of amplifying materials. SOPs have also been recognized as promising lasing materials, although the susceptibility of these materials to optical damage has thus far limited applications. Here we report that attenuated lasing in optically pumped SOP thin films displays a sensitivity to vapours of explosives more than 30 times higher than is observed from spontaneous emission. Critical to this achievement was the development of a transducing polymer with high thin-film quantum yield, a high optical damage threshold in ambient atmosphere and a record low lasing threshold. Trace vapours of the explosives 2,4,6-trinitrotoluene (TNT) and 2,4-dinitrotoluene (DNT) introduce non-radiative deactivation pathways that compete with stimulated emission. We demonstrate that the induced cessation of the lasing action, and associated sensitivity enhancement, is most pronounced when films are pumped at intensities near their lasing threshold. The combined gains from amplifying materials and lasing promise to deliver sensors that can detect explosives with unparalleled sensitivity.

Accelerating boolean satisfiability through application specific processing

This paper presents an application specific multiprocessor system for SAT, utilizing the most recent results such as the development of highly efficient sequential SAT algorithms, the emergence of commercial configurable processor cores and the rapid progress in IC manufacturing techniques. Based on an analysis of the basic SAT search algorithm, we propose a new parallel SAT algorithm that utilizes fine grain parallelism. This is then used to design a multiprocessor architecture in which each processing node consists of a processor and a communication assist node that deals with message processing. Each processor is an application specific processor built from a commercial configurable processor core. All the system configurations are determined based on the characteristics of SAT algorithms, and are supported by simulation results. While this hardware accelerator system does not change the inherent intractability of the SAT problems, it achieves a 30-60x speedup over and above the fastest known SAT solver - Chaff. We believe that this system can be used to expand the practical applicability of SAT in all its application areas.

Chemosensory lasing action for detection of TNT and other analytes

We demonstrate that attenuated luminescence and lasing in optically excited organic thin films is a sensitive probe to vapours of explosives, such as trinitrotoluene (TNT). The combined chemosensing gains from organic amplifying materials and the lasing action, promise to deliver sensors that can detect explosives with unparalleled sensitivity.

Organic electronic device modeling at the nanoscale

Electronic devices with nanoscale features (~ 100 nm or smaller) are becoming increasingly important in electronics technology. While nanoscale electronic devices comprise a variety of different material sets and structures, many of the nanoscale devices developed in the last decade employ organic materials. In this talk, we discuss the modeling of organic electronic thin film devices. In our approach, analysis of such devices begins on the molecular scale, and device level behavior is then derived from the combination of individual molecular properties and physical models of intermolecular interactions. We present a general purpose Monte Carlo simulator based on molecular-scale physical models and employ this simulator to analyze device behavior