Reliable Modeling of Ideal Generic Memristors via State-Space Transformation

The paper refers to problems of modeling and computer simulation of generic memristors caused by the so-called window functions, namely the stick effect, nonconvergence, and finding fundamentally incorrect solutions. A profoundly different modeling approach is proposed, which is mathematically equivalent to window-based modeling. However, due to its numerical stability, it definitely smoothes the above problems away

On the Application of PSpice for Localised Cloud Security

The work reported in this thesis commenced with a review of methods for creating random binary sequences for encoding data locally by the client before storing in the Cloud. The first method reviewed investigated evolutionary computing software which generated noise-producing functions from natural noise, a highly-speculative novel idea since noise is stochastic. Nevertheless, a function was created which generated noise to seed chaos oscillators which produced random binary sequences and this research led to a circuit-based one-time pad key chaos encoder for encrypting data. Circuit-based delay chaos oscillators, initialised with sampled electronic noise, were simulated in a linear circuit simulator called PSpice. Many simulation problems were encountered because of the nonlinear nature of chaos but were solved by creating new simulation parts, tools and simulation paradigms. Simulation data from a range of chaos sources was exported and analysed using Lyapunov analysis and identified two sources which produced one-time pad sequences with maximum entropy. This led to an encoding system which generated unlimited, infinitely-long period, unique random one-time pad encryption keys for plaintext data length matching. The keys were studied for maximum entropy and passed a suite of stringent internationally-accepted statistical tests for randomness. A prototype containing two delay chaos sources initialised by electronic noise was produced on a double-sided printed circuit board and produced more than 200 Mbits of OTPs. According to Vladimir Kotelnikov in 1941 and Claude Shannon in 1945, one-time pad sequences are theoretically-perfect and unbreakable, provided specific rules are adhered to. Two other techniques for generating random binary sequences were researched; a new circuit element, memristance was incorporated in a Chua chaos oscillator, and a fractional-order Lorenz chaos system with order less than three. Quantum computing will present many problems to cryptographic system security when existing systems are upgraded in the near future. The only existing encoding system that will resist cryptanalysis by this system is the unconditionally-secure one-time pad encryption

Fabrication and modeling of thin-film anodic titania memristors

A new method for the fabrication of memristors is exhibited involving the electrochemical anodization of titanium. This is an inexpensive, room temperature alternative to the current methods of fabrication. Two sets of devices were fabricated with varying anodization times and the devices were characterized. One set of the devices was annealed before characterization to evaluate the importance of annealing as stated in papers. The devices not annealed yielded memristive behavior due to oxygen vacancies created at the titanium-TiO2 junction buried below the surface of the device. The annealed devices behaved as resistors because the surface of the TiO2 exposed to the annealing created oxygen vacancies at this interface ensuring both junctions were ohmic. Using an existing model, the best device was modeled by adapting the relevant process parameters

Designing Neuromorphic Computing Systems with Memristor Devices

Deep Neural Networks (DNNs) have demonstrated fascinating performance in many real-world applications and achieved near human-level accuracy in computer vision, natural video prediction, and many different applications. However, DNNs consume a lot of processing power, especially if realized on General Purpose GPUs or CPUs, which make them unsuitable for low-power applications. On the other hand, neuromorphic computing systems are heavily investigated as a potential substitute for traditional von Neumann systems in high-speed low-power applications. One way to implement neuromorphic systems is to use memristor crossbar arrays because of their small size, low power consumption, synaptic like behavior, and scalability. However, these systems are in their early developing stages and still have many challenges to be solved before commercialization. In this dissertation, we will investigate designing of neuromorphic computing systems, targeting classification and generation applications. Specifically, we introduce three novel neuromorphic computing systems. The first system implements a multi-layer feed-forward neural network, where memristor crossbar arrays are utilized in realizing a novel hybrid spiking-based multi-layered self-learning system. This system is capable of on-chip training, whereas for most previously published systems training is done off-chip. The system performance is evaluated using three different datasets showing improved average failure error by 42% than previously published systems and great immunity against process variations. The second system implements an Echo State Network (ESN), as a special type of recurrent neural networks, by utilizing a novel memristor double crossbar architecture. The system has been trained for sample generation, using the Mackey-Glass dataset, and simulations show accurate sample generation within a 75% window size of the training dataset. Finally, we introduce a novel neuromorphic computing for real-time cardiac arrhythmia classification. Raw ECG data is directly fed to the system, without any feature extraction, and hence reducing classification time and power consumption. The proposed system achieves an overall accuracy of 96.17% and requires only 34 ms to test one ECG beat, which outperforms most of its counterparts. For future work, we introduce a preliminary neuromorphic system implementing a deep Generative Adversarial Network (GAN), based on ESNs. The system is called ESN-GAN and it targets natural video generation applications

COMPARISON OF MEMRISTOR MODELS FOR MICROWAVE CIRCUIT SIMULATIONS IN TIME AND FREQUENCY DOMAIN

As reported in the open literature, there are many memristor models for the circuit-level simulations. Some of them are not particularly suitable for microwave circuit simulations. At RF/microwave frequencies, the memristor dynamics become an important issue for the transition process. In this paper we present a number of different SPICE memristor model groups. Each group is explained using representative models, which are analysed and compared from the microwave circuit analysis viewpoint. We consider the model behaviour at RF/microwave frequencies and the memristance setting issues. Results are compared and the best models are recommended

Chemical Wave Computing from Labware to Electrical Systems

Unconventional and, specifically, wave computing has been repeatedly studied in laboratory based experiments by utilizing chemical systems like a thin film of Belousov–Zhabotinsky (BZ) reactions. Nonetheless, the principles demonstrated by this chemical computer were mimicked by mathematical models to enhance the understanding of these systems and enable a more detailedinvestigation of their capacity. As expected, the computerized counterparts of the laboratory based experiments are faster and less expensive. A further step of acceleration in wave-based computingis the development of electrical circuits that imitate the dynamics of chemical computers. A key component of the electrical circuits is the memristor which facilitates the non-linear behavior of the chemical systems. As part of this concept, the road-map of the inspiration from wave-based computing on chemical media towards the implementation of equivalent systems on oscillating memristive circuits was studied here. For illustration reasons, the most straightforward example was demonstrated, namely the approximation of Boolean gates

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing