The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
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An efficient NPN Boolean matching algorithm based on structural signature and Shannon expansion
Cluster Computing, 2018Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm for solving the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the structural signature (SS) vector, which comprises a first-order signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the SS is more effective than the traditional signature. A symmetry mark can distinguish symmetric Variables and asymmetric Variables and be used to search for multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector via Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and phase collision check can be used to discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we test both equivalent and non-equivalent matching speeds on the MCNC benchmark circuit sets and random circuit sets. In the experiment, our algorithm is shown to be 4.2 times faster than competitors when testing equivalent circuits and 172 times faster, on average, when testing non-equivalent circuits.
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An Efficient NPN Boolean Matching Algorithm Based on Structural Signature and Shannon Expansion
arXiv: Data Structures and Algorithms, 2017Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm to solve the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the Structural Signature (SS) vector, which is composed of a 1st signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the structural signature is more effective than is the traditional signature. Two Boolean functions, f and g, may be equivalent when they have the same SS vector. The symmetry mark can distinguish symmetric Variables and asymmetric Variables and search multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector using Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and the phase collision check discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we tested both equivalent and non-equivalent matching peeds on the MCNC benchmark circuit sets and the random circuit sets. In the experiment, our algorithm is two times faster than competitors when testing equivalent circuits and averages at least one hundred times faster when testing non-equivalent circuits. The experimental results show that our approach is highly effective in solving the NPN Boolean matching problem.
Juling Zhang - One of the best experts on this subject based on the ideXlab platform.
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An efficient NPN Boolean matching algorithm based on structural signature and Shannon expansion
Cluster Computing, 2018Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm for solving the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the structural signature (SS) vector, which comprises a first-order signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the SS is more effective than the traditional signature. A symmetry mark can distinguish symmetric Variables and asymmetric Variables and be used to search for multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector via Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and phase collision check can be used to discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we test both equivalent and non-equivalent matching speeds on the MCNC benchmark circuit sets and random circuit sets. In the experiment, our algorithm is shown to be 4.2 times faster than competitors when testing equivalent circuits and 172 times faster, on average, when testing non-equivalent circuits.
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An Efficient NPN Boolean Matching Algorithm Based on Structural Signature and Shannon Expansion
arXiv: Data Structures and Algorithms, 2017Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm to solve the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the Structural Signature (SS) vector, which is composed of a 1st signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the structural signature is more effective than is the traditional signature. Two Boolean functions, f and g, may be equivalent when they have the same SS vector. The symmetry mark can distinguish symmetric Variables and asymmetric Variables and search multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector using Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and the phase collision check discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we tested both equivalent and non-equivalent matching peeds on the MCNC benchmark circuit sets and the random circuit sets. In the experiment, our algorithm is two times faster than competitors when testing equivalent circuits and averages at least one hundred times faster when testing non-equivalent circuits. The experimental results show that our approach is highly effective in solving the NPN Boolean matching problem.
Guowu Yang - One of the best experts on this subject based on the ideXlab platform.
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An efficient NPN Boolean matching algorithm based on structural signature and Shannon expansion
Cluster Computing, 2018Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm for solving the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the structural signature (SS) vector, which comprises a first-order signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the SS is more effective than the traditional signature. A symmetry mark can distinguish symmetric Variables and asymmetric Variables and be used to search for multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector via Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and phase collision check can be used to discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we test both equivalent and non-equivalent matching speeds on the MCNC benchmark circuit sets and random circuit sets. In the experiment, our algorithm is shown to be 4.2 times faster than competitors when testing equivalent circuits and 172 times faster, on average, when testing non-equivalent circuits.
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An Efficient NPN Boolean Matching Algorithm Based on Structural Signature and Shannon Expansion
arXiv: Data Structures and Algorithms, 2017Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm to solve the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the Structural Signature (SS) vector, which is composed of a 1st signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the structural signature is more effective than is the traditional signature. Two Boolean functions, f and g, may be equivalent when they have the same SS vector. The symmetry mark can distinguish symmetric Variables and asymmetric Variables and search multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector using Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and the phase collision check discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we tested both equivalent and non-equivalent matching peeds on the MCNC benchmark circuit sets and the random circuit sets. In the experiment, our algorithm is two times faster than competitors when testing equivalent circuits and averages at least one hundred times faster when testing non-equivalent circuits. The experimental results show that our approach is highly effective in solving the NPN Boolean matching problem.
William N. N. Hung - One of the best experts on this subject based on the ideXlab platform.
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An efficient NPN Boolean matching algorithm based on structural signature and Shannon expansion
Cluster Computing, 2018Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm for solving the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the structural signature (SS) vector, which comprises a first-order signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the SS is more effective than the traditional signature. A symmetry mark can distinguish symmetric Variables and asymmetric Variables and be used to search for multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector via Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and phase collision check can be used to discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we test both equivalent and non-equivalent matching speeds on the MCNC benchmark circuit sets and random circuit sets. In the experiment, our algorithm is shown to be 4.2 times faster than competitors when testing equivalent circuits and 172 times faster, on average, when testing non-equivalent circuits.
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An Efficient NPN Boolean Matching Algorithm Based on Structural Signature and Shannon Expansion
arXiv: Data Structures and Algorithms, 2017Co-Authors: Juling Zhang, Guowu Yang, William N. N. Hung, Yan ZhangAbstract:An efficient pairwise Boolean matching algorithm to solve the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the Structural Signature (SS) vector, which is composed of a 1st signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the structural signature is more effective than is the traditional signature. Two Boolean functions, f and g, may be equivalent when they have the same SS vector. The symmetry mark can distinguish symmetric Variables and asymmetric Variables and search multiple Variable Mappings in a single Variable-Mapping search operation, which reduces the search space significantly. Updating the SS vector using Shannon decomposition provides benefits in distinguishing unidentified Variables, and the group mark and the phase collision check discover incorrect Variable Mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we tested both equivalent and non-equivalent matching peeds on the MCNC benchmark circuit sets and the random circuit sets. In the experiment, our algorithm is two times faster than competitors when testing equivalent circuits and averages at least one hundred times faster when testing non-equivalent circuits. The experimental results show that our approach is highly effective in solving the NPN Boolean matching problem.
Yves Robert - One of the best experts on this subject based on the ideXlab platform.
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PARCO - Mapping uniform loop nests onto distributed memory architectures
Parallel Computing, 1994Co-Authors: Alain Darte, Yves RobertAbstract:Abstract This paper deals with scheduling, Mapping and partitioning techniques for uniform loop nests. Target machines are SPMD distributed memory parallel computers. We use affine-by-statement scheduling and affine-by-Variable Mapping to synthesize a virtual grid architecture from the original loop nest. The virtual grid architecture is then partitioned into a physical processor grid. The key to the Mapping strategy is the communication graph, which enables us to derive optimal Mappings, i.e. where the number of communications is proved to be minimal. The partitioning technique extends the methods developed for systolic array design methodologies to loop nests with several statements.
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ASAP - Communication-minimal Mapping of uniform loop nests onto distributed memory architectures
Proceedings of International Conference on Application Specific Array Processors (ASAP '93), 1Co-Authors: Alain Darte, Yves RobertAbstract:The authors deal with Mapping techniques for uniform loop nests. Target machines are SPMD distributed memory parallel computers. They use affine-by-Variable Mapping to synthesize a virtual grid architecture from the original loop nest. The key to the Mapping strategy is the communication graph, which enables us to derive optimal Mappings, i.e., where the number of communications is proved to be minimal. >
