Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications

Abstract

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.

Behavioral models of nonlinear filters based on discrete time cellular neural networks

“…The dynamics of a cell in the feedforward cellular neural network is obtained by the following differential equation [9]:…”

Lag quasi-synchronization for memristive neural networks with switching jumps mismatch

“…Since the pioneering work by Chua [1], who originally proposed the memristor concept, the research of memristor has become an interesting issue [2][3][4][5]. By replacing the resistors in the artificial neural networks [6][7][8] with memristors, MNNs can be constructed.…”

“…By replacing the resistors in the artificial neural networks [6][7][8] with memristors, MNNs can be constructed. For example, novel memristor-based cellular neural networks were designed in [5], in which the proposed cellular neural networks have high density, nonvolatility and programmability of synaptic weights. Recently, many efforts have been devoted to the dynamics of MNNs, because they not only have rich applications in signal, image processing and diversified applications elsewhere, but also can facilitate to simulate the real brain [9][10][11][12][13][14][15][16][17][18].…”

A generalized family of memristor‐based voltage controlled relaxation oscillator

“…They provide a huge storage where a more powerful type of electronic circuits can be obtained using memristors. They play an important role in many applications 1,2 such as non-volatile memory, [3][4][5] neural networks, 6,7 pattern configuration, 8 reconfigurable logic, 9 cryptography, 10 chaotic circuits, 11,12 and circuit modeling. 13 Oscillators are widely used in electronic applications such as timing circuits, modulation, test, and measurement devices.…”