广义细胞自动机的结构及其硬件实现

该文作者曾提出了广义细胞自动机(GCA)的原理和并行算法．并且应用于网络快速包交换等动态优化问题．该文进一步讨论了这种新的广义细胞自动机的体系结构、算法的硬件实现及其电路设计。它们对于GCA的实际应用有重要意义．GCA结构不同于Hopfield神经网络(HNN)和细胞神经网络(CNN)，GCA由多层次多粒度宏细胞组成塔形结构．它具有多粒度的宏细胞动力学特征．相同粒度宏细胞之间没有交互，但不同粒度宏细胞之间存在一定程度的交互或反馈．分析和实验表明．在问题求解的优化性、实时性、硬件实现复杂性等方面．该文给出的GCA结构和硬件实现．与HNN和CNN相比有诸多优点．

The Architecture and Circuital Implementation Scheme of A New Generalized Cellular Automata

Authors have proposed the conception and parallel algorithm of a generalized cellular automata (GCA) for effectively solving a class of optimization problems in computer networks, such as the Fast Packet Switching problem. This paper further discusses the issues about the architecture,hardware implementation and circuital design scheme of the GCA, which is essential to the applications of GCA approach. Unlike the Hopfield-type neural network (HNN) and cellular neural network (CNN), the proposed GCA has a pyramid architecture that is composed of multi-layer multi-granularity macro-cells, and has the multi-granularity evolution dynamics. In a GCA there is no direct interconnection among the macro-cells with the same granularity, whereas there are some interactions among different macro-cell layers, which not only significantly improves the real-time performance for problem-solving, but also greatly simplifies the hardware structure of GCA. The GCA architecture and its circuital implementation scheme have advantages over the HNN and CNN methods in terms of the real-time performance, interconnection complexity, and parameter selection.