On the Implementation of Cellular Wave Computing Methods by Hardware Learning

Author

Geis, Gunter ; Gollas, Frank ; Tetzlaff, Ronald

Author_Institution

Inst. of Appl. Phys., J. W. Goethe Univ., Frankfurt

fYear

2007

fDate

27-30 May 2007

Firstpage

2930

Lastpage

2933

Abstract

Adaptive signal processing on cellular nonlinear networks (CNN) based electronic devices is an exciting challenge, which needs a fast and robust parameter adaptation. In this contribution implementations and the analysis of optimisation algorithms will be proposed and discussed using the EyeRIStrade hardware system with an embedded ACE16kv2 focal plane processor having 128 times 128 cells. The parameter training performance were analysed in detail.

Keywords

adaptive signal processing; cellular neural nets; embedded systems; focal planes; microprocessor chips; optimisation; EyeRIS hardware system; EyeRIS visual system; adaptive signal processing; cellular nonlinear networks; cellular wave computing; electronic devices; embedded ACE16kv2 focal plane processor; hardware learning; intelligent sensors; optimisation algorithms; Algorithm design and analysis; Analog computers; Cellular networks; Cellular neural networks; Concurrent computing; Hardware; Microprocessors; Optical arrays; Physics; Signal processing algorithms; Cellular Nonlinear Networks; EyeRIS visual system; Intelligent sensors; optimisation algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on

Conference_Location

New Orleans, LA

Print_ISBN

1-4244-0920-9

Electronic_ISBN

1-4244-0921-7

Type

conf

DOI

10.1109/ISCAS.2007.377863

Filename

4253292