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Efficiency considerations for DT-CNN hardware

Malki, Suleyman LU and Spaanenburg, Lambert LU (2007) MWSCAS p.189-192
Abstract
Cellular Neural Networks have become a popular paradigm for modeling nonlinear systems. First-hand implementations are in software on floating-point platforms for pure performance, while programmable analog circuitry has been tested for embedded low-power applications. The paper discusses gradual algorithmic and structural improvements that bring efficient digital hardware into consideration. This provides 32-bits floating-point accuracy on a block-scaled 12-bits fixed-point platform.
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings MWSCAS NEWCAS
pages
189 - 192
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
MWSCAS
conference dates
0001-01-02
external identifiers
  • wos:000257283200049
  • scopus:50049129992
language
English
LU publication?
yes
id
320234db-d179-4691-83b1-953e159a78de (old id 603741)
date added to LUP
2016-04-04 11:55:43
date last changed
2020-11-10 02:57:20
@inproceedings{320234db-d179-4691-83b1-953e159a78de,
  abstract     = {Cellular Neural Networks have become a popular paradigm for modeling nonlinear systems. First-hand implementations are in software on floating-point platforms for pure performance, while programmable analog circuitry has been tested for embedded low-power applications. The paper discusses gradual algorithmic and structural improvements that bring efficient digital hardware into consideration. This provides 32-bits floating-point accuracy on a block-scaled 12-bits fixed-point platform.},
  author       = {Malki, Suleyman and Spaanenburg, Lambert},
  booktitle    = {Proceedings MWSCAS NEWCAS},
  language     = {eng},
  pages        = {189--192},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Efficiency considerations for DT-CNN hardware},
  year         = {2007},
}