An implementation of a fully connected artificial neural network using the multilayered perceptron model is described. The neural network is implemented on a systolic array processor based on the Geometric Arithmetic Parallel Processor (GAPP) chip. Arrays of GAPP chips make up a single-instruction multiple-data (SIMD) class machine which has fine-grained connections and is fully programmable. Previous application areas of the GAPP system are image/signal processing, computer vision, and knowledge-based processing. The neural network is a relatively new processing model for the GAPP, but one that readily maps onto the architecture of the overall array processor. The proof-of-concept neural network is a multilayered perceptron model which uses the back-propagation learning paradigm. This initial network has fewer than 100 nodes in three layers and is trained to recognize letters of the alphabet.<<ETX>>
[1]
J. Hopfield,et al.
Computing with neural circuits: a model.
,
1986,
Science.
[2]
D.E. Goldberg,et al.
Classifier Systems and Genetic Algorithms
,
1989,
Artif. Intell..
[3]
William P. Jones,et al.
Back Propagation
,
1987,
Principles of Artificial Neural Networks.
[4]
Richard P. Lippmann,et al.
An introduction to computing with neural nets
,
1987
.
[5]
Stephen Grossberg,et al.
Associative Learning, Adaptive Pattern Recognition, And Cooperative-Competitive Decision Making By Neural Networks
,
1986,
Other Conferences.
[6]
J J Hopfield,et al.
Collective computation in neuronlike circuits.
,
1987,
Scientific American.
[7]
Stephen Grossberg,et al.
Competitive Learning: From Interactive Activation to Adaptive Resonance
,
1987,
Cogn. Sci..