Spin-L: sequential pipelined neuroemulator with learning capabilities

In this paper, an extensive study of learning and retrieval algorithms for Hopfield's pattern classifier network, multilayer backpropagation, Kohonen's self-organized feature mapping network, and the binary adaptive resonance theory (ART-1) models is reported. Parallelism as well as computational requirements are identified for all algorithms. The algorithms are then mapped onto the sequential pipelined neuroemulator (SPIN) architecture. As a result, the SPIN with learning (SPIN-L) machine is developed as an enhanced architecture to accommodate the new requirements.