Programmable current mode Hebbian learning neural network using Programmable Metallization Cell

B. Swaroop; W. C. West; G. Martinez; Michael Kozicki; L. A. Akers

Programmable current mode Hebbian learning neural network using Programmable Metallization Cell

B. Swaroop, W. C. West, G. Martinez, Michael Kozicki, L. A. Akers

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Scopus citations

Abstract

The design and performance of a Hebbian learning based neural network is presented in this work. In situ analog learning was employed, thus computing the synaptic weight changes continuously during the normal operation of the artificial neural network (ANN). The complexity of a synapse is minimized by using a novel device called the Programmable Metallization Cell (PMC). Simulations with circuits with three PMCs per synapse showed that appropriate learning behaviour was obtained at the synaptic level.

Original language	English (US)
Title of host publication	Proceedings - IEEE International Symposium on Circuits and Systems
Editors	Anon
Publisher	IEEE
Pages	33-36
Number of pages	4
Volume	3
State	Published - 1998
Event	Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS. Part 5 (of 6) - Monterey, CA, USA Duration: May 31 1998 → Jun 3 1998

Other

Other	Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS. Part 5 (of 6)
City	Monterey, CA, USA
Period	5/31/98 → 6/3/98

ASJC Scopus subject areas

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Cite this

Swaroop, B, West, WC, Martinez, G, Kozicki, M & Akers, LA 1998, Programmable current mode Hebbian learning neural network using Programmable Metallization Cell. in Anon (ed.), Proceedings - IEEE International Symposium on Circuits and Systems. vol. 3, IEEE, pp. 33-36, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS. Part 5 (of 6), Monterey, CA, USA, 5/31/98.

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year = "1998",

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editor = "Anon",

booktitle = "Proceedings - IEEE International Symposium on Circuits and Systems",

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AU - West, W. C.

AU - Martinez, G.

AU - Kozicki, Michael

AU - Akers, L. A.

PY - 1998

Y1 - 1998

N2 - The design and performance of a Hebbian learning based neural network is presented in this work. In situ analog learning was employed, thus computing the synaptic weight changes continuously during the normal operation of the artificial neural network (ANN). The complexity of a synapse is minimized by using a novel device called the Programmable Metallization Cell (PMC). Simulations with circuits with three PMCs per synapse showed that appropriate learning behaviour was obtained at the synaptic level.

AB - The design and performance of a Hebbian learning based neural network is presented in this work. In situ analog learning was employed, thus computing the synaptic weight changes continuously during the normal operation of the artificial neural network (ANN). The complexity of a synapse is minimized by using a novel device called the Programmable Metallization Cell (PMC). Simulations with circuits with three PMCs per synapse showed that appropriate learning behaviour was obtained at the synaptic level.

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M3 - Conference contribution

AN - SCOPUS:0031618186

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BT - Proceedings - IEEE International Symposium on Circuits and Systems

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Y2 - 31 May 1998 through 3 June 1998

ER -

Programmable current mode Hebbian learning neural network using Programmable Metallization Cell

Abstract

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ASJC Scopus subject areas

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