DocumentCode
2286236
Title
Configurable multi-layer CNN-UM emulator on FPGA
Author
Nagy, Zoltan ; Szolgay, Peter
Author_Institution
Dept. of Image Process. & Neurocomputing, Univ. of Veszprem, Hungary
fYear
2002
fDate
22-24 Jul 2002
Firstpage
164
Lastpage
171
Abstract
A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. In this paper the main steps of the FPGA implementation are introduced. Main results are as follows: CNN-UM architecture emulated on Xilinx Virtex series FPGA, 3D non-linear spatio-temporal dynamics can be implemented on this architecture. The critical parameters of the implementation in single layer configuration are 55 million cell update/second/processor core or equivalently 1 GOPS computing performance. In face of the high performance the power requirements of the architecture are relatively low only ∼3 W per processor core. Using re-configurable devices to implement emulated digital architectures provides more flexibility compared to the custom VLSI designs because different Falcon architectures can be used on the same FPGA device.
Keywords
cellular neural nets; field programmable gate arrays; multilayer perceptrons; neural chips; neural net architecture; reconfigurable architectures; 3D nonlinear spatio-temporal dynamics; CNN Universal Machine; CNN-UM chip architecture; FPGA; Falcon; Xilinx Virtex series FPGA; cellular neural network; computing performance; configurable multilayer CNN-UM emulator; emulated digital architectures; power requirements; reconfigurable devices; Analog computers; Cellular neural networks; Computer architecture; Equations; Field programmable gate arrays; Laboratories; Signal processing; State feedback; Turing machines; Very large scale integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and Their Applications, 2002. (CNNA 2002). Proceedings of the 2002 7th IEEE International Workshop on
Print_ISBN
981-238-121-X
Type
conf
DOI
10.1109/CNNA.2002.1035049
Filename
1035049
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