DocumentCode :
1615085
Title :
Performance analysis and improvements on a hybrid cascade architecture for multi-layer neural networks
Author :
Nosratinia, Aria ; Ahmadi, M. ; Sridhar, M.
Author_Institution :
Cooordinated Sci., Lab., Illinois Univ., Urbana, IL, USA
fYear :
1992
Firstpage :
1214
Abstract :
A series of improvements in a hybrid architecture for multilayer networks is presented. This architecture incorporates the incoming connection strengths and the neurons of each layer into one stage by a multiplexing scheme, hence reducing the complexity of interstage wiring. An analysis of the performance of this architecture is performed and, based on its results, the authors propose a number of improvements. Also, a three-layer network has been implemented in a double metal, single polysilicon p-well CMOS technology based on the proposed improvements. The performance of the improved version is analyzed and compared to the original structure. Bounds on the operating speed of the system are also presented
Keywords :
CMOS integrated circuits; feedforward neural nets; multiplexing; neural chips; parallel architectures; performance evaluation; double-metal single-poly p-well CMOS; hybrid cascade architecture; multilayer neural nets; multiplexing scheme; performance analysis; three-layer network; CMOS technology; Counting circuits; Feedforward neural networks; Feedforward systems; Multi-layer neural network; Neural networks; Neurons; Performance analysis; Power engineering and energy; Read only memory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1992., Proceedings of the 35th Midwest Symposium on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-0510-8
Type :
conf
DOI :
10.1109/MWSCAS.1992.271053
Filename :
271053
Link To Document :
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