Presentation	2011-05-27 Learning of binary neural networks for logical synthesis Yuta NAKAYAMA, Ryo ITO, Toshimichi SAITO,
PDF Download Page	PDF download Page Link
Abstract(in Japanese)	(See Japanese page)
Abstract(in English)	This paper studies a learning algorithm of binary neural networks (BNN) and its application to logical synthesis. The network can approximate a desired Boolean function if parameters are selected suitably. Comparing the BNN with a typical logical synthesis method, We can clarify that the BNN can be equivalent to the minimum disjunctive canonical form in some parameter subspace. Outside of the subspace, the BNN can be simpler than the minimum form. Performing typical numerical experiment, the algorithm efficiency is confirmed.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Binary Neural networks / Logic synthesis
Paper #	NLP2011-11
Date of Issue

Conference Information
Committee	NLP
Conference Date	2011/5/19(1days)
Place (in Japanese)	(See Japanese page)
Place (in English)
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To	Nonlinear Problems (NLP)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Learning of binary neural networks for logical synthesis
Sub Title (in English)
Keyword(1)	Binary Neural networks
Keyword(2)	Logic synthesis
1st Author's Name	Yuta NAKAYAMA
1st Author's Affiliation	Graduate School of Engineering, Hosei University()
2nd Author's Name	Ryo ITO
2nd Author's Affiliation	Graduate School of Engineering, Hosei University
3rd Author's Name	Toshimichi SAITO
3rd Author's Affiliation	Graduate School of Engineering, Hosei University
Date	2011-05-27
Paper #	NLP2011-11
Volume (vol)	vol.111
Number (no)	62
Page	pp.pp.-
#Pages	5
Date of Issue