An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training

Abstract

Memristive neuromorphic systems are emerging potential hardware platforms to implement artificial neural networks. Combining features of memristive neuromorphic systems with associative memory, this article proposes an associative-memory-based reconfigurable memristive neuromorphic system. In the proposed system, there are two neural networks: 1) the neural network for computing acceleration and 2) the neural network mimicking associative memory. Then, a case study of the system is presented, which includes an associative memory network to realize apple recognition and a computing acceleration network for iris classification. The associative memory network depends on associative learning to achieve the recognition function. In addition, during the corresponding forgetting process, the connections of the related synaptic circuits are cut off and sent to a synaptic circuit block, realizing variable circuit topology. Further, the synaptic circuits in the block are applied to construct the iris classification network, accomplishing the reconfiguration of the proposed system. The circuit structure of this classification network matches backpropagation (BP) algorithm well. Meanwhile, the network reaches a relatively high classification accuracy after training. In an iteration of the training, all the synaptic circuits that need to change can adjust weights synchronously, which improves training speed.