An FPGA Implementation of a Hopfield Optimized Block Truncation Coding

Saif, Sherif M.; Abbas, Hazem M.; Nassar, Salwa M.; Wahdan, Abdelmonem A.

doi:10.1109/iwsoc.2006.348230

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Several FPGA implementations of ANNs have been reported in the literature [1][2][3][4][5]. Of special interest is the FPGA design proposed by Leiner et al [1], because it implements the same Hopfield neural model.…”

Section: Performance and Comparison With Previous Workmentioning

confidence: 99%

“…Artificial Neural Networks (ANN's) have become a subject of very dynamic and extensive research [1][2][3][4]. One important factor is the progress in VLSI technology, which makes easier the implementation and testing of ANNs in ways not available in the past.…”

Section: Introductionmentioning

confidence: 99%

“…There are many publications in the literature concerning the implementation of Hopfield Neural Network (HNN) in FPGAs (Field Programmable Gates Arrays). In [2] an implementation of HNN on Xilinx VirtexE is used for block truncation coding for image/video compression. Reference [4] describes an implementation of HNN on FPGA (Virtex-4LX160) for the identification of symmetrically structured DNA motifs in Alpha Data, which has better performance than the same algorithms implemented in C++ on a IBM X260 Server.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Implementation on FPGA of a Hopfield Neural Network

Mansour

Ayoubi

Ziade

et al. 2011

Advances in Artificial Neural Systems

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The associative Hopfield memory is a form of recurrent Artificial Neural Network (ANN) that can be used in applications such as pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. This paper presents the implementation of the Hopfield Neural Network (HNN) parallel architecture on a SRAM-based FPGA. The main advantage of the proposed implementation is its high performance and cost effectiveness: it requires O(1) multiplications and O(log N) additions, whereas most others require O(N) multiplications and O(N) additions.

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