Design method for Unconventional Computing
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/603757
- author
- Spaanenburg, Lambert LU ; Åkesson, B ; Hansson, A and Goossens, K
- organization
- publishing date
- 2006
- 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}}, }