An Architecture for Neural Network Parallel Processor
Qin Wang · Journal of Chinese Computer Systems · 2007
If a neural network processing system can realize more kinds of neural network,its commonality is much better and it can be used in more application fields.We propose an architecture for neural network parallel processor to broaden its application fields,which can realize the BP neural network and the Hopfield neural network with a higher parallelism.The processor is based on SIMD(Single Instruction Multiple Data)architecture.Combining the characters of the two algorithms,we design systolic array and a full interconnection net to realize the data sharing more easily and more flexibly.The experiment result shows that the runtime of the neural network is enhanced effectively.