Associative memory network and its hardware design

• A novel associative memory network is proposed.
• An improved learning rule is designed for the associative network.
• The hardware design of the associative network is given.
• Associative memories for strong correlation and multi-value patterns are done.

In order to improve the performance of the conventional associative memory network, a novel associative memory network composed of input layer, computing layer, associative layer and output layer is proposed. An improved Hebb learning rule is designed for the associative network to perform the associative memory of strong correlation and multi-valued sample patterns. The associative memory can be performed by the associative network in only one forward calculation. The hardware circuit of the network can be designed by simple devices to ensure its parallel computation ability and meet the real-time requirement. Simulation results show that the network has better associative performance than conventional associative network in the binary patterns associative memory, it can store and associate strong correlation sample patterns, and it can retrieve the distortion multi-valued sample patterns with 40% noise correctly.