Parallel Implementation of Self-Organizing Map on the Partial Tree Shape Neurocomputer
Neural Processing Letters
Parallel implementation of self-organizing maps
Self-Organizing neural networks
NeuSim: A Modular Neural Networks Simulator for Beowulf Clusters
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
A hierarchical RBF online learning algorithm for real-time 3-D scanner
IEEE Transactions on Neural Networks
A scalable pipelined architecture for real-time computation of MLP-BP neural networks
Microprocessors & Microsystems
Artificial Intelligence Review
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This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications