Expansible and Reconfigurable Neuro Informatics Engine : ERNIE

대규모 확장이 가능한 범용 신경회로망 : ERNIE

One of the hardest problems in implementation of digital neural network are extension of synapses and programmability for relocating neurons. This paper Proposes a new hardware structure to solve these problems. The proposed structure can reconfigure network connections without alteration of basic design, and extend number of synapses attached to one neuron. Also, it is possible to extend the number of neurons and layers by connecting many MPUs(Modular Processing Unit). Generality and extensibility are verified by composing various kinds of Perceptorn and Kohonen networks using the architecture proposed in this paper and the verification performances compares well with HDL simulation results as well as the results of C modelling.