Projection-field-type VLSI convolutional neural networks using merged/mixed analog-digital approach

Osamu Nomura, Takashi Morie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The hierarchical convolutional neural network models are considered promising for robust object detection/recognition. These models require huge computational power for performing a large number of multiply-and-accumulation (MAC) operations. In this paper, first we discuss efficient calculation schemes suitable for 2D MAC operations. Then we review the related algorithms and LSI architecture proposed in our previous work, in which we use a projection-field-type network architecture with sorting of neuron outputs by magnitude. For the LSI implementation, we adopt a merged/mixed analog-digital circuit approach using a large number of analog or pulse modulation circuits. We demonstrate the validity of our LSI architecture by testing proof-of-concept LSIs. It is essential to develop efficient and parallel A/D and D/A conversion circuits in order to connect a lot of on-chip analog circuits with the external digital system. In this paper, we also propose such an A/D conversion circuit scheme.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages1081-1090
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 2007 Nov 132007 Nov 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4984 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Neural Information Processing, ICONIP 2007
Country/TerritoryJapan
CityKitakyushu
Period07/11/1307/11/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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