Abstract
Cellular Neural Networks (CNN) is a massive computing paradigm which became very popular in the last decades. A Cellular Neural Network Universal Machine is an extension of the CNN concept. An implementation of CNN-UM on Field Programmable Gate Arrays (FPGA) appears attractive because their full computational power comes to a life only in hardware. Besides FPGA there are many different possibilities to implement a CNN-UM. The following questions will be answered while reading this paper: What is the CNN paradigm? Which application areas are of interest and what requirements are to meet? What is a CNN-UM? Which ways are possible to implement a CNN-UM - what are the differences? Which problems occur while implementing a CNN-UM on FPGA?
Originalsprache | Englisch (Amerika) |
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Titel | VXV International Symposium on Theoretical Engineering |
Seiten | 1-5 |
Seitenumfang | 5 |
Publikationsstatus | Veröffentlicht - 24 Juni 2009 |
Veranstaltung | VXV International Symposium on Theoretical Engineering - Lübeck, Germany Dauer: 22 Juni 2009 → 24 Juni 2009 |
Konferenz
Konferenz | VXV International Symposium on Theoretical Engineering |
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Zeitraum | 22/06/09 → 24/06/09 |
Schlagwörter
- Field programmable gate arrays
- Robustness
- Hardware
- Arrays
- Physics
- Application specific integrated circuits
- Computational modeling