An Optimal Implementation on FPGA of a Hopfield Neural Network | Zendy

Wassim Mansour | Zendy; Rami Ayoubi | Zendy; Haissam Ziade | Zendy; Raoul Velazco | Zendy; Wassim El Falou | Zendy

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An Optimal Implementation on FPGA of a Hopfield Neural Network

Author(s) -

Wassim Mansour,

Rami Ayoubi,

Haissam Ziade,

Raoul Velazco,

Wassim El Falou

Publication year - 2011

Publication title -

advances in artificial neural systems

Language(s) - English

Resource type - Journals

eISSN - 1687-7608

pISSN - 1687-7594

DOI - 10.1155/2011/189368

Subject(s) - algorithm , computer science , artificial neural network , artificial intelligence , machine learning , database

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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