Paper Info
Title
An efficient and expandable hardware implementation of multilayer cellular neural networks.
Abstract
This paper proposes a new CNN architecture conceived for hardware implementation of complex ML-CNNs on programmable devices. The architecture is completely modular and expandable, and includes advanced features such as non-linear templates, time-variant coefficients or multi-layer structure. We also present an implementation platform based on the pre-designed but user-configurable FPGA processing modules that inherit the modularity and expandability of the logical architecture. All the modules share the same, properly designed, I/O interface, so the platform can be configured to accommodate CNNs of any size or structure, composed of a number of processing blocks that can be physically distributed over several FPGA boards. Our Carthagonova architecture makes use of a temporal processing approach with a super-pipelined unfolded cell structure, leading to the maximum degree of parallelism while still keeping the most efficient use of FPGA resources. Both the CNN architecture and the hardware platform have been validated by the implementation of a real-time video processing system, showing that they conform a valuable set of tools for the development of CNN-based applications.
Year
DOI
Venue
2013
10.1016/j.neucom.2012.09.029
Neurocomputing
Keywords
Field
DocType
Multi FPGA-based implementation,Cellular neural network,Modular architecture
Computer architecture,Architecture,Video processing,Applications architecture,Computer science,Field-programmable gate array,Modular design,Template,Computer hardware,Cellular neural network,Modularity,Embedded system
Journal
Volume
ISSN
Citations 
114
0925-2312
13
PageRank 
References 
Authors
0.69
8
4
Name
Order
Citations
PageRank
J. Javier Martínez1355.78
F. Javier Garrigós2184.15
F. Javier Toledo317518.14
José Manuel Ferrández418628.38