A Massively Parallel Architecture for Self-Organizing Feature Maps

Porrmann M, Witkowski U, Rückert U (2003)
IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations 14(5): 1110-1121.

Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
Zeitschriftenaufsatz | Veröffentlicht | Englisch
Abstract / Bemerkung
A hardware accelerator for self-organizing feature maps is presented. We have developed a massively parallel architecture that, on the one hand, allows a resource-efficient implementation of small or medium-sized maps for embedded applications, requiring only small areas of silicon. On the other hand, large maps can be simulated with systems that consist of several integrated circuits that work in parallel. Apart from the learning and recall of self-organizing feature maps, the hardware accelerates data pre- and postprocessing. For the verification of our architectural concepts in a real-world environment, we have implemented an ASIC that is integrated into our heterogeneous multiprocessor system for neural applications. The performance of our system is analyzed for various simulation parameters. Additionally, the performance that can be achieved with future microelectronic technologies is estimated.
Erscheinungsjahr
Zeitschriftentitel
IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations
Band
14
Zeitschriftennummer
5
Seite
1110-1121
ISSN
PUB-ID

Zitieren

Porrmann M, Witkowski U, Rückert U. A Massively Parallel Architecture for Self-Organizing Feature Maps. IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations. 2003;14(5):1110-1121.
Porrmann, M., Witkowski, U., & Rückert, U. (2003). A Massively Parallel Architecture for Self-Organizing Feature Maps. IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations, 14(5), 1110-1121. doi:10.1109/TNN.2003.816368
Porrmann, M., Witkowski, U., and Rückert, U. (2003). A Massively Parallel Architecture for Self-Organizing Feature Maps. IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations 14, 1110-1121.
Porrmann, M., Witkowski, U., & Rückert, U., 2003. A Massively Parallel Architecture for Self-Organizing Feature Maps. IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations, 14(5), p 1110-1121.
M. Porrmann, U. Witkowski, and U. Rückert, “A Massively Parallel Architecture for Self-Organizing Feature Maps”, IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations, vol. 14, 2003, pp. 1110-1121.
Porrmann, M., Witkowski, U., Rückert, U.: A Massively Parallel Architecture for Self-Organizing Feature Maps. IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations. 14, 1110-1121 (2003).
Porrmann, Mario, Witkowski, Ulf, and Rückert, Ulrich. “A Massively Parallel Architecture for Self-Organizing Feature Maps”. IEEE Transactions on Neural Networks, Special Issue on Hardware Implementations 14.5 (2003): 1110-1121.

3 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Improved Learning Performance of Hardware Self-Organizing Map Using a Novel Neighborhood Function.
Hikawa H, Maeda Y., IEEE Trans Neural Netw Learn Syst 26(11), 2015
PMID: 26484943
Color clustering and learning for image segmentation based on neural networks.
Dong G, Xie M., IEEE Trans Neural Netw 16(4), 2005
PMID: 16121733

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 18244564
PubMed | Europe PMC

Suchen in

Google Scholar