Title :
An application-specific array architecture for feedforward with backpropagation ANNs
Author :
Malluhi, Q.M. ; Bayoumi, M.A. ; Rao, T.R.N.
Author_Institution :
Center for Adv. Comput. Studies, Univ. of Southwestern Louisiana, Lafayette, LA, USA
Abstract :
An application-specific array architecture for Artificial Neural Networks (ANNs) computation is proposed. This array is configured as a mesh-of-appendixed-trees (MAT). Algorithms to implement both the recall and the training phases of the multilayer feedforward with backpropagation ANN model are developed on MAT. The proposed MAT architecture requires only O(log N) time, while other reported techniques offer O(N) time, where N is the size of the largest layer. Beside the high speed performance, pipelining of more than one input pattern can be achieved which further improves the performance
Keywords :
application specific integrated circuits; backpropagation; feedforward neural nets; neural chips; parallel architectures; pipeline processing; application-specific array architecture; backpropagation ANNs; feedforward; high speed performance; input pattern; mesh-of-appendixed-trees; multilayer feedforward; pipelining; training phases; Artificial neural networks; Backpropagation; Computational modeling; Computer architecture; Computer networks; Concurrent computing; Neural networks; Neurons; Parallel processing; Pipeline processing;
Conference_Titel :
Application-Specific Array Processors, 1993. Proceedings., International Conference on
Conference_Location :
Venice
Print_ISBN :
0-8186-3492-8
DOI :
10.1109/ASAP.1993.397156