VLSI design of cellular neural networks with annealing and optical input capabilities

Bing J. Sheu; Sa H. Bang; Wai-Chi  Fang

doi:10.1109/ISCAS.1995.521598

VLSI design of cellular neural networks with annealing and optical input capabilities

Bing J. Sheu^*, Sa H. Bang, Wai-Chi Fang

^*Corresponding author for this work

Institute of Electronics

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

A cellular neural network (CNN) is a locally connected, massively paralleled computing system with simple synaptic operators so that it is very suitable for VLSI implementation in real-time, high-speed applications. VLSI architecture of a continuous-time shift-invariant CNN with digitally-programmable operators and optical inputs is proposed. The circuits with annealing ability is included to achieve optimal solutions for many selected applications.

Original language	English
Pages (from-to)	653-659
Number of pages	7
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	1
DOIs	https://doi.org/10.1109/ISCAS.1995.521598
State	Published - 1 Jan 1995
Event	Proceedings of the 1995 IEEE International Symposium on Circuits and Systems-ISCAS 95. Part 3 (of 3) - Seattle, WA, USA Duration: 30 Apr 1995 → 3 May 1995

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10.1109/ISCAS.1995.521598

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title = "VLSI design of cellular neural networks with annealing and optical input capabilities",

abstract = "A cellular neural network (CNN) is a locally connected, massively paralleled computing system with simple synaptic operators so that it is very suitable for VLSI implementation in real-time, high-speed applications. VLSI architecture of a continuous-time shift-invariant CNN with digitally-programmable operators and optical inputs is proposed. The circuits with annealing ability is included to achieve optimal solutions for many selected applications.",

author = "Sheu, {Bing J.} and Bang, {Sa H.} and Wai-Chi Fang",

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AU - Bang, Sa H.

AU - Fang, Wai-Chi

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N2 - A cellular neural network (CNN) is a locally connected, massively paralleled computing system with simple synaptic operators so that it is very suitable for VLSI implementation in real-time, high-speed applications. VLSI architecture of a continuous-time shift-invariant CNN with digitally-programmable operators and optical inputs is proposed. The circuits with annealing ability is included to achieve optimal solutions for many selected applications.

AB - A cellular neural network (CNN) is a locally connected, massively paralleled computing system with simple synaptic operators so that it is very suitable for VLSI implementation in real-time, high-speed applications. VLSI architecture of a continuous-time shift-invariant CNN with digitally-programmable operators and optical inputs is proposed. The circuits with annealing ability is included to achieve optimal solutions for many selected applications.

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DO - 10.1109/ISCAS.1995.521598

M3 - Conference article

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SN - 0271-4310

VL - 1

SP - 653

EP - 659

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

T2 - Proceedings of the 1995 IEEE International Symposium on Circuits and Systems-ISCAS 95. Part 3 (of 3)

Y2 - 30 April 1995 through 3 May 1995

ER -

VLSI design of cellular neural networks with annealing and optical input capabilities

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