Publication Details
Title: Considerations for the Electronic Implementation of Artificial Neural Networks
Author: N. Morgan
Group: ICSI Technical Reports
Date: January 1990
PDF: http://www.icsi.berkeley.edu/pubs/techreports/tr-90-03.pdf
Overview:
Computer scientists and designers have long been interested in comparisons between artificial automata and the human brain [Von Neumann, 1957]. Mental activity is often characterized as the result of the parallel operation of large numbers of neurons (~10 superscript 11 for the human brain). Neurons interact electrochemically on a time scale of milliseconds, and are jointly capable of significant feats of pattern recognition (such as recognizing a friend wearing an unusual costume). These commonplace human achievements are currently unattainable by large electronic computers built from components with characteristic delays in the nanosecond range. Artificial Neural Network (ANN) researchers hope that simplified functional models of nervous tissue can help us to design algorithms and machines that are better than conventional computers for difficult problems in machine perception and intelligence. However, engineering constraints for silicon implementations of these systems may suggest design choices which differ from mimicry of biology in significant ways. In particular, large silicon ANN systems may require multiplexing of communication AND CO and computation as a consequence of limited connectivity. This report discusses considerations such as these, and concludes with a short description of an ongoing effort to design silicon ANN building blocks using powerful CAD tools.
Bibliographic Information:
ICSI Technical Report TR-90-003
Bibliographic Reference:
N. Morgan. Considerations for the Electronic Implementation of Artificial Neural Networks. ICSI Technical Report TR-90-003, January 1990
Author: N. Morgan
Group: ICSI Technical Reports
Date: January 1990
PDF: http://www.icsi.berkeley.edu/pubs/techreports/tr-90-03.pdf
Overview:
Computer scientists and designers have long been interested in comparisons between artificial automata and the human brain [Von Neumann, 1957]. Mental activity is often characterized as the result of the parallel operation of large numbers of neurons (~10 superscript 11 for the human brain). Neurons interact electrochemically on a time scale of milliseconds, and are jointly capable of significant feats of pattern recognition (such as recognizing a friend wearing an unusual costume). These commonplace human achievements are currently unattainable by large electronic computers built from components with characteristic delays in the nanosecond range. Artificial Neural Network (ANN) researchers hope that simplified functional models of nervous tissue can help us to design algorithms and machines that are better than conventional computers for difficult problems in machine perception and intelligence. However, engineering constraints for silicon implementations of these systems may suggest design choices which differ from mimicry of biology in significant ways. In particular, large silicon ANN systems may require multiplexing of communication AND CO and computation as a consequence of limited connectivity. This report discusses considerations such as these, and concludes with a short description of an ongoing effort to design silicon ANN building blocks using powerful CAD tools.
Bibliographic Information:
ICSI Technical Report TR-90-003
Bibliographic Reference:
N. Morgan. Considerations for the Electronic Implementation of Artificial Neural Networks. ICSI Technical Report TR-90-003, January 1990