Translator Disclaimer
21 September 1998 Analog VLSI implementation of a morphological associative memory
Author Affiliations +
The theory and application of morphological associative memories and morphological neural networks in general are emerging areas of research in computer science. The concept of a morphological associative memory differs from a more conventional associative memory by the nonlinear functionality of the synaptic connection. By taking the maximum of sums instead of the sum of products, morphological network computation is inherently nonlinear. Hence, the morphological associative memory does not require any ad hoc methodology to interject a nonlinear state. In this paper, we introduce a very large scale integration analog circuit design that describes the nonlinear functionality of the synaptic connection. We specifically describe the fundamental circuit needed to implement a basic additive maximum associative memory, and describe noise conditions under which this memory will perform flawlessly. As a potential application, we propose the use of the analog circuit to real-time operation on or near a focal plane array sensor.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James R. Stright, Patrick C. Coffield, and Geoffrey W. Brooks "Analog VLSI implementation of a morphological associative memory", Proc. SPIE 3452, Parallel and Distributed Methods for Image Processing II, (21 September 1998); doi: 10.1117/12.323470;


Stochastic associative memory
Proceedings of SPIE (August 18 1993)
Analog VLSI motion projects at Caltech
Proceedings of SPIE (December 18 1996)
Analog CMOS contrastive Hebbian networks
Proceedings of SPIE (September 15 1992)

Back to Top