A Vector Orthogonal Multiprocessor NEOMP and its Use in Neural Network Mapping
José Hiroki Saito · 1999
A vector Orthogonal Multiprocessor architecture NEOMP and its use by a feedforward artificial neural network, Neocognitron, is described. The proposed architecture is composed by several vector processing units, and a scalar control processor, which access the memory modules in a orthogonal fashion. The performance analysis of the architecture is realized, identifying the concurrent computation grains in the neocognitron, which are attributed to the vector processors. The analysis of the architecture showed that its speed-up is linear in a wide range, where the implementation of NEOMP is appropriate. The scalar control processor and the vector processing unit hardware prototype were simulated and showed the feasibility of their implementation, each one in a single FPGA, which inspire the construction of NEOMP as a real time neocognitron, and other feedforward neural network systems, using vector orthogonal multiprocessor architecture.