Lneuro 1.0: a piece of hardware LEGO for building neural network systems

摘要

Neural network simulations on a parallel architecture are reported. The architecture is scalable and flexible enough to be useful for simulating various kinds of networks and paradigms. The computing device is based on an existing coarse-grain parallel framework (INMOS transputers), improved with finer-grain parallel abilities through VLSI chips, and is called the Lneuro 1.0 (for LEP neuromimetic) circuit. The modular architecture of the circuit makes it possible to build various kinds of boards to match the expected range of applications or to increase the power of the system by adding more hardware. The resulting machine remains reconfigurable to accommodate a specific problem to some extent. A small-scale machine has been realized using 16 Lneuros, to experimentally test the behavior of this architecture. Results are presented on an integer version of Kohonen feature maps. The speedup factor increases regularly with the number of clusters involved (to a factor of 80). Some ways to improve this family of neural network simulation machines are also investigated.