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.

中文翻译：

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一种基于联想记忆的可重构记忆神经形态系统，具有同步重量训练

忆阻神经形态系统是实现人工神经网络的新兴潜在硬件平台。本文结合忆阻神经形态系统和联想记忆的特点，提出了一种基于联想记忆的可重构忆阻神经形态系统。在所提出的系统中，有两个神经网络：1）用于计算加速的神经网络和 2）模仿联想记忆的神经网络。然后，介绍了该系统的案例研究，其中包括一个用于实现苹果识别的联想记忆网络和一个用于虹膜分类的计算加速网络。联想记忆网络依靠联想学习来实现识别功能。此外，在相应的遗忘过程中，相关突触电路的连接被切断，送入突触电路块，实现可变电路拓扑。此外，块中的突触电路用于构建虹膜分类网络，完成所提出系统的重构。这个分类网络的电路结构很好地匹配了反向传播（BP）算法。同时，网络经过训练后达到了比较高的分类准确率。在训练的一次迭代中，所有需要改变的突触回路都可以同步调整权重，从而提高训练速度。这个分类网络的电路结构很好地匹配了反向传播（BP）算法。同时，网络经过训练后达到了比较高的分类准确率。在训练的一次迭代中，所有需要改变的突触回路都可以同步调整权重，从而提高训练速度。这个分类网络的电路结构很好地匹配了反向传播（BP）算法。同时，网络经过训练后达到了比较高的分类准确率。在训练的一次迭代中，所有需要改变的突触回路都可以同步调整权重，从而提高训练速度。