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Design method for Unconventional Computing

Spaanenburg, Lambert LU ; Åkesson, B ; Hansson, A and Goossens, K (2006) 10th Int. Workshop on Cellular Neural Networks and their Applications (CNNA) p.334-339
Abstract
Network-on-Chip concepts allow moving away from the classical, centralized computer. The meaningful collaboration of computing units over a well-behaved network creates the infinite state space that underlies the Turing computer concept. The combinatorial state explosion that results from a Network-on-Chip will easily prove more valuable than the mere technological progress in memory storage, when the network nodes become small enough. Embedded Super-Computing has this unconventional goal. This paper introduces a design method for such computing concepts. It discusses how a software simulation is gradually migrated into a Network-on-Chip implementation. The approach is illustrated by the development of a Cellular Neural Network as a... (More)
Network-on-Chip concepts allow moving away from the classical, centralized computer. The meaningful collaboration of computing units over a well-behaved network creates the infinite state space that underlies the Turing computer concept. The combinatorial state explosion that results from a Network-on-Chip will easily prove more valuable than the mere technological progress in memory storage, when the network nodes become small enough. Embedded Super-Computing has this unconventional goal. This paper introduces a design method for such computing concepts. It discusses how a software simulation is gradually migrated into a Network-on-Chip implementation. The approach is illustrated by the development of a Cellular Neural Network as a typical example of a well-behaved network on small, embedded nodes. (Less)
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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Cellular Neural Networks, Network-on-Chip, System-on-Silicon., Field programmable gate arrays
host publication
Proceedings 10th IEEE Workshop on CNNA and their Applications
pages
334 - 339
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
10th Int. Workshop on Cellular Neural Networks and their Applications (CNNA)
conference dates
2006-08-28 - 2006-08-30
external identifiers
  • wos:000245392200071
  • scopus:47549109241
ISBN
1-4244-0640-4
DOI
10.1109/CNNA.2006.341655
language
English
LU publication?
yes
id
22b6ee6c-a3ba-4981-b72a-5c92b05f4673 (old id 603757)
date added to LUP
2016-04-04 09:52:21
date last changed
2022-01-29 19:26:37
@inproceedings{22b6ee6c-a3ba-4981-b72a-5c92b05f4673,
  abstract     = {{Network-on-Chip concepts allow moving away from the classical, centralized computer. The meaningful collaboration of computing units over a well-behaved network creates the infinite state space that underlies the Turing computer concept. The combinatorial state explosion that results from a Network-on-Chip will easily prove more valuable than the mere technological progress in memory storage, when the network nodes become small enough. Embedded Super-Computing has this unconventional goal. This paper introduces a design method for such computing concepts. It discusses how a software simulation is gradually migrated into a Network-on-Chip implementation. The approach is illustrated by the development of a Cellular Neural Network as a typical example of a well-behaved network on small, embedded nodes.}},
  author       = {{Spaanenburg, Lambert and Åkesson, B and Hansson, A and Goossens, K}},
  booktitle    = {{Proceedings 10th IEEE Workshop on CNNA and their Applications}},
  isbn         = {{1-4244-0640-4}},
  keywords     = {{Cellular Neural Networks; Network-on-Chip; System-on-Silicon.; Field programmable gate arrays}},
  language     = {{eng}},
  pages        = {{334--339}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Design method for Unconventional Computing}},
  url          = {{http://dx.doi.org/10.1109/CNNA.2006.341655}},
  doi          = {{10.1109/CNNA.2006.341655}},
  year         = {{2006}},
}