Dept. of Computer Science and Engineering
Oregon Graduate Institute of Science & Technology
Before artificial neural network applications become common there must be inexpensive hardware that will allow large networks to be run in real time. It is uncertain how large networks will do when constrained to implementations on architectures of current technology. Some tradeoffs must be made when the network models are implemented efficiently. Three popular artificial neural network models are analyzed. This paper discusses the effects on performance when the models are modified for efficient hardware implementation.
Baker, Thomas Edward, "Implementation limits for artificial neural networks" (1990). Scholar Archive. 162.