Title :
SNAP: a parallel processor for implementing real-time neural networks
Author :
Wojciechowski, Edward
Author_Institution :
ITT Avionics, Nutley, NJ, USA
Abstract :
The author describes the Scalable Neural Array Processor (SNAP), a single-instruction/multiple data (SIMD) parallel processing architecture for solving artificial neural systems, including Hopfield networks, adaptive resonance theory networks, multi-layer perceptron with back error propagation, and bidirectional associative memories. SNAP offers a low-cost, viable, neural network processing engine for real-time applications and research
Keywords :
cellular arrays; content-addressable storage; neural nets; parallel architectures; real-time systems; Hopfield networks; SIMD; SNAP; Scalable Neural Array Processor; adaptive resonance theory networks; artificial neural systems; bidirectional associative memories; brassboard; cellular array; multi-layer perceptron with back error propagation; neural network processing engine; parallel processor; real-time neural networks; single layer nearest neighbor encoder; single-instruction/multiple data; Application software; Artificial neural networks; Biological neural networks; Computational modeling; Computer architecture; Computer networks; Computer simulation; Humans; Neural networks; Neurons;
Conference_Titel :
Aerospace and Electronics Conference, 1991. NAECON 1991., Proceedings of the IEEE 1991 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-0085-8
DOI :
10.1109/NAECON.1991.165834
