Xiaofang Hu

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

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.

Analog memristive memory with applications in audio signal processing

Since the development of the HP memristor, much attention has been paid to studies of memristive devices and applications, particularly memristor-based nonvolatile semiconductor memory. Owing to its unique properties, theoretically, one could restart a memristor-based computer immediately without the need for reloading the data. Further, current memories are mainly binary and can store only ones and zeros, whereas memristors have multilevel states, which means a single memristor unit can replace many binary transistors and realize higher-density memory. It is believed that memristors can also implement analog storage besides binary and multilevel information memory. In this paper, an implementation scheme for analog memristive memory is considered. A charge-controlled memristor model is derived and the corresponding SPICE model is constructed. Special write and read operations are demonstrated through numerical analysis and circuit simulations. In addition, an audio analog record/play system using a memristor crossbar array is designed. This system can provide great storage capacity (long recording time) and high audio quality with a simple small circuit structure. A series of computer simulations and analyses verify the effectiveness of the proposed scheme.

Memristive crossbar array with applications in image processing

A memristor is a kind of nonlinear resistor with memory capacity. Its resistance changes with the amount of charge or flux passing through it. As the fourth fundamental circuit element, it has huge potential applications in many fields, and has been expected to drive a revolution in circuit theory. Through numerical simulations and circuitry modeling, the basic theory and properties of memristors are analyzed, and a memristorbased crossbar array is then proposed. The array can realize storage and output for binary, grayscale and color images. A series of computer simulations demonstrates the effectiveness of the proposed scheme. Owing to the advantage of the memristive crossbar array in parallel information processing, the proposed method is expected to be used in high-speed image processing.

Memristor-based RRAM with applications

Recently acclaimed the fourth fundamental circuit element, the memristor was theoretically predicted by Leon Chua in 1971, although its single device electronic implementation eluded the attention of integrated circuit designers for the past three decades and was first reported in 2008 by the Hewlett-Packard (HP) Laboratory researchers while developing crossbar-based ultra high-density nonvolatile memories. Memristor-based hybrid nanoscale CMOS technology is expected not only to impact the flash memory industries profoundly, but also to revolutionize digital and neuromorphic computing. The memristor exhibits a dynamical resistance state that depends on its excitation history and which can be exploited to build transistor-less nonvolatile semiconductor memory (NVSM), commonly known as resistive RAM (RRAM). This paper addresses an implementation scheme for memristor-based resistive random access memory (MRRAM), a nano-scale binary memory that is compatible with modern computer systems. Its structure is similar to that of static random access memory (SRAM), but with the memristor replacing the underlying RS flip-flop. By improving the MRRAM, we propose a multilevel memory with greater data density, which stores multiple bit information in gray-scale form in a memory unit. Reported computer simulations and numerical analyses verify the effectiveness of the proposed scheme in storing ASCII characters and gray-scale images in binary format.

An EDA framework for large scale hybrid neuromorphic computing systems

In implementations of neuromorphic computing systems (NCS), memristor and its crossbar topology have been widely used to realize fully connected neural networks. However, many neural networks utilized in real applications often have a sparse connectivity, which is hard to be efficiently mapped to a crossbar structure. Moreover, the scale of the neural networks is normally much larger than that can be offered by the latest integration technology of memristor crossbars. In this work, we propose AutoNCS - an EDA framework that can automate the NCS designs that combine memristor crossbars and discrete synapse modules. The connections of the neural networks are clustered to improve the utilization of the memristor elements in crossbar structures by taking into account the physical design cost of the NCS. Our results show that AutoNCS can substantially enhance the utilization efficiency of memristor crossbars while reducing the wirelength, area and delay of the physical designs of the NCS.

Multilayer RTD-memristor-based cellular neural networks for color image processing

Multilayer cellular neural networks (CNNs) with multiple state variables in each cell associated with multiple dynamic rules are believed to possess more powerful data computing and signal processing capabilities than single-layer CNNs and are specially suitable for solving complex problems. However, at present, their large scale integrated hardware implementation is still quite challenging based on traditional CMOS-based technology due to high circuity complexity and their applications are thus limited in practice. In this paper, a novel compact multilayer CNN model based on nanometer scale resonant tunneling diodes (RTDs) and memristors is presented. More specifically, in this model, one multilayer CNN cell consists of several sub-cells located in different layers. The resonant tunneling diode with quantum tunneling induced nonlinearity and uniquely folded current-voltage characteristics is used to implement the compact and high-speed cell via replacing the original linear resistor and removing the output function of conventional CNN cells. Furthermore, the interactions between these cells are determined by a pair of multi-dimensional cloning templates. And a compact synaptic circuit based on memristors is designed to realize the cloning template parameter (weight strength) and the multiplication (weighting) operation, by leveraging its nonvolatility, good scalability, and variable conductance. The combination of these desirable elements equips the proposed multilayer CNN with advantages of powerful processing capability as well as high compactness, versatility, and possibility of very large scale integration (VLSI) circuit implementations. Finally, the performance of the proposed multilayer CNN is validated by five illustrative examples in color image processing with each layer dealing with each primary color (red, green or blue) plane. Nanoscale RTDs and memristors improve the circuit integration and resolution.Multilayer architecture provides more powerful processing ability and versatility.The memristor with thresholds promises effective writing and undisturbed reading.

Small-world Hopfield neural networks with weight salience priority and memristor synapses for digit recognition

Abstract A novel systematic design of associative memory networks is addressed in this paper, by incorporating both the biological small-world effect and the recently acclaimed memristor into the conventional Hopfield neural network. More specifically, the original fully connected Hopfield network is diluted by considering the small-world effect, based on a preferential connection removal criteria, i.e., weight salience priority. The generated sparse network exhibits comparable performance in associative memory but with much less connections. Furthermore, a hardware implementation scheme of the small-world Hopfield network is proposed using the experimental threshold adaptive memristor (TEAM) synaptic-based circuits. Finally, performance of the proposed network is validated by illustrative examples of digit recognition.

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.

Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing

Cellular neural network (CNN) has been acted as a high-speed parallel analog signal processor gradually. However, recently, since the decrease in the size of transistor is going to approach the utmost, the transistor-based integrated circuit technology hits a bottleneck. As a result, the advantage of very large scale integration implementation of CNN becomes hard to really present, and further development of this era faces severe challenges unavoidably. In this study, two types of memristor-based cellular neural networks have been proposed. One type uses a memristor to replace the linear resistor in a conventional CNN cell circuit. And the other places a resonant tunneling diode (RTD) in this position and uses memristive synaptic connections to structure a hybrid memristor RTD CNN model. The excellent performances of the proposed CNNs are verified by conventional means of, for instance, stability analysis and efficient applications in image processing. Since both the memristor and the resonant tunneling diode are nanoscale, the size of the network circuits can be greatly reduced, and the integration density of the system will be significantly improved.

A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.