Di Marco Mauro, Forti Mauro, Pancioni Luca
Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Neural Netw. 2017 Sep;93:152-164. doi: 10.1016/j.neunet.2017.05.009. Epub 2017 May 24.
The paper introduces a class of memristor neural networks (NNs) that are characterized by the following salient features. (a) The processing of signals takes place in the flux-charge domain and is based on the time evolution of memristor charges. The processing result is given by the constant asymptotic values of charges that are stored in the memristors acting as non-volatile memories in steady state. (b) The dynamic equations describing the memristor NNs in the flux-charge domain are analogous to those describing, in the traditional voltage-current domain, the dynamics of a standard (S) cellular (C) NN, and are implemented by using a realistic model of memristors as that proposed by HP. This analogy makes it possible to use the bulk of results in the SCNN literature for designing memristor NNs to solve processing tasks in real time. Convergence of memristor NNs in the presence of multiple asymptotically stable equilibrium points is addressed and some applications to image processing tasks are presented to illustrate the real-time processing capabilities. Computing in the flux-charge domain is shown to have significant advantages with respect to computing in the voltage-current domain. One advantage is that, when a steady state is reached, currents, voltages and hence power in a memristor NN vanish, whereas memristors keep in memory the processing result. This is basically different from SCNNs for which currents, voltages and power do not vanish at a steady state, and batteries are needed to keep in memory the processing result.
本文介绍了一类忆阻器神经网络（NNs），其具有以下显著特征。（a）信号处理在磁通-电荷域中进行，基于忆阻器电荷的时间演化。处理结果由稳态下作为非易失性存储器的忆阻器中存储的电荷的恒定渐近值给出。（b）在磁通-电荷域中描述忆阻器神经网络的动态方程类似于在传统电压-电流域中描述标准（S）细胞（C）神经网络动态的方程，并通过使用如惠普公司提出的忆阻器实际模型来实现。这种类比使得可以利用SCNN文献中的大量结果来设计忆阻器神经网络以实时解决处理任务。讨论了存在多个渐近稳定平衡点时忆阻器神经网络的收敛性，并给出了一些图像处理任务的应用以说明实时处理能力。结果表明，在磁通-电荷域中的计算相对于在电压-电流域中的计算具有显著优势。一个优势是，当达到稳态时，忆阻器神经网络中的电流、电压以及因此的功率都消失了，而忆阻器却将处理结果存储下来。这与SCNN基本不同，对于SCNN，在稳态时电流、电压和功率不会消失，并且需要电池来存储处理结果。
