Memristor-based cellular nanoscale networks: Theory, circuits, and applications

In this paper, the theory, circuit design, and possible applications of Cellular Nanoscale Networks (CNNs) which are based on memristor technology are reviewed. In the memristor-based CNNs, memristors can be used to realize the analog multiplication circuit that is essential in performing the computation functions of CNNs with low-power consumption and small area. Compared to the memristor-based crossbar architecture that can be used to mimic the fundamental neuron-cell-level operation such as Spike Time Dependent Plasticity (STDP), the memristor-based CNN circuit is more suitable in mimicking the advanced sensory systems such as image processing of human's retina. In this paper, we explain the basics of CNN computation at first and we discuss the previous memristor-based CNN circuits that are very useful in performing analog multiplication. And, also, we think of some practical issues of CNN circuits and discuss the possible solutions. For the CNN applications using memristors, we show the simulation results of CNN circuit with Laplacian template that can be used in the edge detection of various images.