This paper presents a new compact canonical-based algorithm to solve the problem of singleoutput completely specified NPN Boolean matching.We propose a new signature vector Boolean difference and cofactor (DC) signature vector. Our algorithm utilizes the Boolean difference, cofactor signature and symmetry properties to search for canonical transformations. The use of symmetry and Boolean difference notably reduces the search space and speeds up the Boolean matching process compared to the algorithm proposed in [1]. We tested our algorithm on a large number of circuits. The experimental results showed that the average runtime of our algorithm 37% higher and its average search space 67% smaller compared to [1] when tested on general circuits.

InIn this paper, we describe a new verification method to accelerate the input negation and/or input permutation and/or output negation (NPN) Boolean matching for a single-output completely specified Boolean function. Through research on the Boolean Shannon decomposition binary tree, we prove that the signature vectors of the left child node and right child node are complementary relative to the signature vector of the parent node.We introduce an independent variable check to speed up the detection of candidate transformation. The proposed approach utilises this complementarity, a symmetry check, an independent variable check and a phase collision check, which can quickly verify whether the candidate transformation obtained in the detection of the candidate transformation of the Boolean matching process is accurate. We perform experiments on two types of Boolean function sets. One type consists of Boolean functions from randomly generated circuits. The other is exported from the Microelectronics Center of North Carolina (MCNC) benchmark. The experimental results show that the average runtime of our algorithm is 68.8% faster than those in Reference [1] on two randomly generated circuits and 51% faster than those in Reference [1] when tested on the MCNC benchmark circuit set. Therefore, the experimental results demonstrate the effectiveness of the proposed method.

In this paper, we address an NPN Boolean matching algorithm. The proposed structural difference signature (SDS) of a Boolean function significantly reduces the search space in the Boolean matching process. The paper analyses the size of the search space from three perspectives: the total number of possible transformations, the number of candidate transformations and the number of decompositions. We test the search space and run time on a large number of randomly generated circuits and Microelectronics Center of North Carolina (MCNC) benchmark circuits with 7–22 inputs. The experimental results show that the search space of Boolean matching is greatly reduced and the matching speed is obviously accelerated.

In view of the current lack of unified security authentication and control for the power Internet of Things terminal equipment, at the perception level of the power Internet of Things, the perception layer terminal access control, front-end authentication technology realization and terminal equipment abnormal behavior detection methods are proposed. This method enhances the communication security between power equipment and edge nodes, and ensures the safe and stable operation of the power Internet of Things.

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.