The effects of precision constraints in a backpropagation learning network

Authors:
Paul W. Hollis;John S. Harper;John J. Paulos
Affiliations:
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7911 USA;Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7911 USA;Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7911 USA
Venue:
Neural Computation
Year:
1990

Citing 2
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Abstract

This paper presents a study of precision constraints imposed by a hybrid chip architecture with analog neurons and digital backpropagation calculations. Conversions between the analog and digital domains and weight storage restrictions impose precision limits on both analog and digital calculations. It is shown through simulations that a learning system of this nature can be implemented in spite of limited resolution in the analog circuits and using fixed point arithmetic to implement the backpropagation algorithm.