On limited fan-in optimal neural networks

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Because VLSI implementations do not cope well with highly interconnected nets the area of a chip growing as the cube of the fan-in--this paper analyses the influence of limited fan in on the size and VLSI optimality of such nets. Two different approaches will show that VLSI- and size-optimal discrete neural networks can be obtained for small (i.e. lower than linear) fan-in values. They have applications to hardware implementations of neural networks. The first approach is based on implementing a certain sub class of Boolean functions, IF{sub n,m} functions. The authors will show that this class of functions can be ... continued below

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15 p.

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Beiu, V.; Makaruk, H.E. & Draghici, S. March 1, 1998.

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Description

Because VLSI implementations do not cope well with highly interconnected nets the area of a chip growing as the cube of the fan-in--this paper analyses the influence of limited fan in on the size and VLSI optimality of such nets. Two different approaches will show that VLSI- and size-optimal discrete neural networks can be obtained for small (i.e. lower than linear) fan-in values. They have applications to hardware implementations of neural networks. The first approach is based on implementing a certain sub class of Boolean functions, IF{sub n,m} functions. The authors will show that this class of functions can be implemented in VLSI optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan ins. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on neural networks with fan-ins limited to 2. They generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan in values, while relative minimum size solutions can be obtained for fan-ins strictly lower than linear. Finally, a size-optimal neural network having small constant fan-ins will be suggested for IF{sub n,m} functions.

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15 p.

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OSTI as DE98004666

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  • 4. Brasilian symposium on neural networks, Boifnia (Brazil), 3-5 Dec 1997

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  • Other: DE98004666
  • Report No.: LA-UR--97-4314
  • Report No.: CONF-971235--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 654140
  • Archival Resource Key: ark:/67531/metadc709203

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  • March 1, 1998

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  • Sept. 12, 2015, 6:31 a.m.

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  • May 20, 2016, 3:04 p.m.

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Beiu, V.; Makaruk, H.E. & Draghici, S. On limited fan-in optimal neural networks, article, March 1, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc709203/: accessed April 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.