Hardware efficient learning on a 3-D optoelectronic neural system

Ashok V. Krishnamoorthy, Stephen A. Brodsky, Clark C. Guest, Gary C. Marsden, Matthias Blume, Gokce Yayla, Jean Merckle, Sadik C. Esener

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

We discuss the Dual-Scale Topology Optoelectronic Processor (D-STOP) neural network, a scalable, optically interconnected neural network architecture. We present the tandem D-STOP system, which provides the connectivity needed for building fully-parallel neural networks with generic gradient-descent learning rules. We review the Content Addressable Network (CAN) learning algorithm, a discrete learning algorithm that provides accelerated learning with reduced hardware requirements. We then show how the CAN algorithm can be effectively mapped onto D-STOP, and we investigate associated optoelectronic hardware tradeoffs.

Original languageEnglish (US)
Pages1998-2003
Number of pages6
StatePublished - Dec 1 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

ASJC Scopus subject areas

  • Software

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    Krishnamoorthy, A. V., Brodsky, S. A., Guest, C. C., Marsden, G. C., Blume, M., Yayla, G., Merckle, J., & Esener, S. C. (1994). Hardware efficient learning on a 3-D optoelectronic neural system. 1998-2003. Paper presented at Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7), Orlando, FL, USA, .