This paper presents algorithms for cryptographic algorithms mapping based on the coarse-grained reconfigurable cryptographic logic array. Due to a long mapping period and low performance for current algorithms mapping

we propose two methods to improve it. First

combine with the structural characteristics of the coarse-grained reconfigurable cryptographic logic array and cryptographic algorithms

an algorithm for data flow graph partitioning is proposed. By integrating the nodes into clusters to reduce the mapping complexity. Second

refer to the idea of machine learning

a smart ant colony optimization algorithm is proposed. By learning the training samples

the pheromone concentration matrix is continuously optimized and realizes the intelligentization of cryptographic algorithm mapping. The experimental results show that the proposed mapping method can reduce the compilation time by 37.9% and achieve the best performance. At the same time

the algorithm data flow graph is used as the mapping input

and the cryptographic algorithm map stream is automatically generated

which improves the cryptographic algorithm mapping more intuitive and convenient.