Title :
A reconfigurable `ANN´ architecture
Author :
Madraswala, T.H. ; Mohd, B.J. ; Ali, M. ; Premi, R. ; Bayoumi, M.A.
Author_Institution :
Center for Adv. Comput. Studies, Univ. of Southwestern Louisiana, Lafayette, LA, USA
Abstract :
Proposes a design of a digital artificial neural network (ANN). The architecture is based on a single-instruction multiple-data (SIMD) processing configuration. Communication is done through broadcasting and also by systolic methods. With the help of a microprogrammed control unit, the design is mainly capable of implementing the following three models: (1) Hamming, (2) Hopfield, and (3) Carpenter/Grossberg algorithms. The architecture was also designed to achieve parallelism, modularity, adaptability, flexibility, speed, low cost, smaller silicon area, and expandability
Keywords :
Hopfield neural nets; VLSI; neural chips; parallel architectures; Carpenter/Grossberg algorithms; Hamming algorithms; Hopfield algorithms; SIMD processing; adaptability; digital artificial neural network; expandability; flexibility; microprogrammed control unit; modularity; silicon area; speed; systolic methods; Artificial neural networks; Computer architecture; Computer networks; Costs; Integrated circuit interconnections; Machine learning; Neural networks; Neurons; Silicon; Very large scale integration;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.230198
