2D neural hardware versus 3D biological ones

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Description

This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological ... continued below

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8 pages

Creation Information

Beiu, V. December 1998.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Los Alamos National Laboratory
    Publisher Info: Los Alamos National Lab., Space and Atmospheric Div., NM (United States)
    Place of Publication: New Mexico

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Description

This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

Physical Description

8 pages

Notes

OSTI as DE00304136

Source

  • International symposium on neural computation, Vienna (AT), 09/23/1998--09/25/1998; Other Information: Supercedes report DE99001244; PBD: [1998]

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  • Other: DE99001244
  • Report No.: LA-UR--98-2504
  • Report No.: CONF-980966--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 304136
  • Archival Resource Key: ark:/67531/metadc679390

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Creation Date

  • December 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • May 5, 2016, 6:16 p.m.

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Beiu, V. 2D neural hardware versus 3D biological ones, article, December 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc679390/: accessed September 26, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.