An important theoretic interest is to study the relations between different interconnection networks, and to compare the capability and performance of the network structures. The most popular way to do the investigati...An important theoretic interest is to study the relations between different interconnection networks, and to compare the capability and performance of the network structures. The most popular way to do the investigation is network emulation. Based on the classical voltage graph theory, the authors develop a new representation scheme for interconnection network structures. The new approach is a combination of algebraic methods and combinatorial methods. The results demonstrate that the voltage graph theory is a powerful tool for representing well known interconnection networks and in implementing optimal network emulation algorithms, and in particular, show that all popular interconnection networks have very simple and intuitive representations under the new scheme. The new representation scheme also offers powerful tools for the study of network routings and emulations. For example, we present very simple constructions for optimal network emulations from the cube connected cycles networks to the butterfly networks, and from the butterfly networks to the hypercube networks. Compared with the most popular way of network emulation, this new scheme is intuitive and easy to realize, and easy to apply to other network structures.展开更多
Spike neural networks are inspired by animal brains,and outperform traditional neural networks on complicated tasks.However,spike neural networks are usually used on a large scale,and they cannot be computed on commer...Spike neural networks are inspired by animal brains,and outperform traditional neural networks on complicated tasks.However,spike neural networks are usually used on a large scale,and they cannot be computed on commercial,off-the-shelf computers.A parallel architecture is proposed and developed for discrete-event simulations of spike neural networks.Furthermore,mechanisms for both parallelism degree estimation and dynamic load balance are emphasized with theoretical and computational analysis.Simulation results show the effectiveness of the proposed parallelized spike neural network system and its corresponding support components.展开更多
基金TheNationalScienceFundforOverseasDistinguishedYoungScholars (No .6 992 82 0 1) ,FoundationforUniversityKeyTeacherbytheMinistryofEducationandChangjiangScholarRewardProject.
文摘An important theoretic interest is to study the relations between different interconnection networks, and to compare the capability and performance of the network structures. The most popular way to do the investigation is network emulation. Based on the classical voltage graph theory, the authors develop a new representation scheme for interconnection network structures. The new approach is a combination of algebraic methods and combinatorial methods. The results demonstrate that the voltage graph theory is a powerful tool for representing well known interconnection networks and in implementing optimal network emulation algorithms, and in particular, show that all popular interconnection networks have very simple and intuitive representations under the new scheme. The new representation scheme also offers powerful tools for the study of network routings and emulations. For example, we present very simple constructions for optimal network emulations from the cube connected cycles networks to the butterfly networks, and from the butterfly networks to the hypercube networks. Compared with the most popular way of network emulation, this new scheme is intuitive and easy to realize, and easy to apply to other network structures.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61003082,60921062,61005077)
文摘Spike neural networks are inspired by animal brains,and outperform traditional neural networks on complicated tasks.However,spike neural networks are usually used on a large scale,and they cannot be computed on commercial,off-the-shelf computers.A parallel architecture is proposed and developed for discrete-event simulations of spike neural networks.Furthermore,mechanisms for both parallelism degree estimation and dynamic load balance are emphasized with theoretical and computational analysis.Simulation results show the effectiveness of the proposed parallelized spike neural network system and its corresponding support components.
