Quantum computing is a compelling new technology that is becoming increasingly practical as time progresses. Quantum computers have the potential to solve problems of great complexity and magnitude across many different industries, utilizing appropriate quantum algorithms. Given the evolving stage of quantum computing, characterized by a limited number of operational quantum computers that demand extensive cooling, face decoherence issues, and incur significant fabrication costs, there exists a pronounced need for quantum computer simulators. In this work, a reconfigurable closed-loop CBRAM crossbar system has been developed for the execution and acceleration of quantum algorithm simulation, leveraging analog in-memory computing. The circuit supports a universal set of quantum gates representation, and through its reprogramming capabilities and feedback loop, can compute any quantum algorithm. To demonstrate its functionality, the 3-qubit Grover algorithm is executed on the proposed circuit. Building on the extensive circuit simulation, a comparative analysis of power and speed between the proposed nanoelectronic circuit and conventional hardware has been conducted, demonstrating the high efficiency and performance gains, followed by a scalability analysis. Furthermore, a framework has been developed that supports the design of custom closed-loop memristive crossbars through a graphical user interface (GUI), providing the user with the capability to execute quantum algorithms and examine the programming and computations of the circuit, assisting with the realization of a hardware prototype.

This paper presents the applicability of a mycelium-inspired reservoir network through an innovative extension of the Memristive Oscillating Cellular Automata (MOCA) circuit-based network. Drawing on the adaptive, self-organizing properties of mycelium, this MOCA grid employs SiNx-based RRAMs to form reconfigurable, dynamic connections that replicate mycelial network behaviors. Using the Stanford-PKU RRAM model, the non-linear properties of the fabricated devices have been characterized, establishing a flexible reservoir network capable of transforming and encoding input signals. The network has been evaluated, demonstrating small-world characteristics, including high clustering and short average path lengths, critical for effective information propagation and complex local dynamics. The resulting adaptable circuit offers a scalable foundation for future applications in bioinspired reservoir computing.

Mycelium, the vegetative structure of fungi, exhibits complex electrical signaling patterns when stimulated that resemble neural-like activity, which can be leveraged for bio-inspired computing and sensing applications. To replicate this activity, a 100x100 grid-based circuit has been designed capable of spiking behavior and adaptable configuration thanks to memristive technology aligned with fabricated devices, mimicking the dynamics seen in mycelial networks. Simulations have been carried out to demonstrate that the memristive grid successfully replicates key aspects of mycelium’s electrical activity recorded from experimental setups, including response to environmental stimuli, spiking signal propagation, and eradication.

Abstract Mycelium, the intricate vegetative network of fungi, has emerged as a promising candidate within the realm of engineered living materials (ELMs). While its intriguing structural and electrical properties highlight its potential, mycelium growth is highly sensitive to environmental conditions. To bridge this gap, a robust framework was developed to both model mycelium growth and explore its computational capabilities. This framework uses a cellular automata (CA) approach, enhanced with reaction-diffusion (RD) processes, to simulate mycelium growth under diverse environmental conditions. This configuration, combined with tunable parameters, enables the identification and validation of optimal growth patterns, supported by an algorithm designed to extract key features of hyphae–the fundamental building blocks of the mycelial network. Subsequently, the small-world properties of the modeled mycelium networks were investigated, revealing high clustering coefficients and short path lengths, characteristics that make them well-suited for reservoir computing (RC). To demonstrate their computational capabilities, mycelium-inspired RC architectures were evaluated on the MNIST dataset classification task, achieving an accuracy of up to 97.09%, highlighting the effectiveness of biologically inspired models. As a result, this framework establishes a comprehensive test-bench for mycelium modeling, growth, and computational exploration, paving the way for innovative applications in bio-inspired computing.

In the era of high-performance computing, the integration of Cellular Automata (CA) principles into low-power hardware is a challenging but intriguing endeavor. At the same time, memristors have gained attention due to their potential in in-memory neuromorphic computing. As such, the concept of Wave Cellular Automata (WCA) is presented a novel computing paradigm that leverages CA principles and memristive devices for in-memory computing. However, memristive devices are subject to variability effects, which can impact their performance and, in the case of WCAs, the generation of oscillations crucial for computation. This paper explores the variability tolerance analysis of WCA both in CBRAM device level, but also in its oscillatory behavior. The analysis reveals that WCA operation remains robust even in the presence of variability, with the impact on oscillation amplitude being minor. All in all, proper circuit design and element selection play a significant role in mitigating the effects of variability.

A swarm of miniature robots comprises mini-robots collaborating to achieve common goals, inspired by the collective behavior of insects. This concept mimics decentralized cooperation, enabling complex task accomplishment across various fields such as healthcare, exploration, and rescue missions. Mini-robots operate in confined spaces and time frames with minimal energy, requiring efficient path planning to prevent collisions within their group and surroundings. Integrating ultra-low-power electronics in these robots is essential. Memristors, renowned for their low power consumption, simplicity, and high integration density, hold significant promise. This paper proposes an integrated collision avoidance memristive circuit with neuromorphic behavior for miniature robots. This circuit not only enhances swarm efficiency but also lays the groundwork for fully analog mini-robots.

Abstract The use of Cellular Automata (CA) in combination with Learning Automata (LA) has demonstrated effectiveness in handling hard-to-be-solved problems. Due to their capacity to learn and adapt, as well as their inherent parallelism, they can expedite the problem-solving process for a range of problems, such as challenging logic puzzles. One such puzzle is Sudoku, which poses a combinatorial optimization challenge of great difficulty and complexity. In this study, a Sudoku puzzle was represented as an Irregular Learning Cellular Automaton (ILCA), using a reward and penalty algorithm to resolve it. Simulations for an amount of 400 puzzles were performed, while the results demonstrate that the proposed algorithm operates effectively, highlighting the concurrent and learning capabilities of the ILCA structure. Furthermore, two different performance enhancement methods are investigated, namely learning rates method and selective probability reset rule, which are able to increase the initial performance by $$26.8\%$$ 26.8 % and to achieve an overall $$99.3\%$$ 99.3 % resolution rate.

There is a continuous urge for higher efficiency in conventional computing systems, driven by an ever-growing demand for these systems’ complexity to be able to match the one of convoluted and challenging problems. However, this type of problems has formulated the benchmarks for unconventional computing systems to validate their emerging applicability and prove their effectiveness. Towards this path, Cellular Automata (CAs) have been established as a promising mathematical tool for simulating physical processes and demonstrated a favourable methodology for effectively implementing computations in hardware by taking advantage of their inherent parallelism. Representing CAs with oscillating memristive networks could further enhance the performance of these systems, by incorporating the rich dynamics evident in memristors and their strong memory and computing features. In this work, a wave generator circuit has been designed with low-voltage fabricated CBRAM devices, that is able to act as a Wave Cellular Automaton (WCA). These wave generation units are located on a grid with adjusting multi-directional interconnections between neighbors. In addition to that, the ability to reconFigure the amount of such units that influence each other, facilitates the propagation of voltage signals through the grid following wave propagation features. An example of this computational domain is presented with the realization of complex logic gates on the grid of WCAs.

The threshold switching effect is critical in memristor devices for a range of applications, from crossbar design reliability to simulating neuromorphic features using artificial neural networks. The rich inherit dynamics of a metallic conductive filament (CF) formation are thought to be linked to this characteristic. Simulating these dynamics is necessary to develop an accurate memristor model. In this work we present a compact memristor model that utilizes the drift, diffusion and thermo-diffusion effects. These three effects are taken into consideration to derive the switching behavior of a memristor. The resistance of a memristor is calculated based on the evolution of a truncated cone shaped filament. The objective of this model is to achieve a realistic integration of switching mechanisms of the memristor device, while minimizing the overhead on computing resources and being compatible with circuit design tools. The model incorporates the effect of thermo-diffusion on the switching pattern, providing a different perception of the ionic transport processes, which enable the unipolar switching. SPICE simulation results provide an exact match with experimental results of Metal-Insulator-Metal (MIM) memristive devices of Ag/Si2/SiO2.07/Pt nanoparticles (NPs) configuration.

There is a growing interest in quantum computers and quantum algorithm development. It has been proved that ideal quantum computers, with zero error rates and large decoherence times, can solve problems that are intractable for today’s classical computers. Quantum computers use two resources, superposition and entanglement, that have no classical analog. Since quantum computer platforms that are currently available comprise only a few dozen of qubits, the use of quantum simulators is essential in developing and testing new quantum algorithms. We present a novel quantum simulator based on memristor crossbar circuits and use them to simulate well-known quantum algorithms, namely the Deutsch and Grover quantum algorithms. In quantum computing the dominant algebraic operations are matrix-vector multiplications. The execution time grows exponentially with the simulated number of qubits, causing an exponential slowdown in quantum algorithm execution using classical computers. In this work, we show that the inherent characteristics of memristor arrays can be used to overcome this problem and that memristor arrays can be used not only as independent quantum simulators but also as a part of a quantum computer stack where classical computers accelerators are connected. Our memristive crossbar circuits are re-configurable and can be programmed to simulate any quantum algorithm.

Over the last years there has been significant progress in the development of quantum computers. It has been demonstrated that they can accelerate the solution of various problems exponentially compared to today's classical computers, harnessing the properties of superposition and entanglement, two resources that have no classical analog. Since quantum computer platforms that are currently available comprise only a few tenths of qubits, as well as the access to a fabricated quantum computer is time limited for the majority of researchers, the use of quantum simulators is essential in developing and testing new quantum algorithms. Taking inspiration from previous work on developing a novel quantum simulator based on memristor crossbar circuits, in this work, a framework that automates the circuit design of emulated quantum gates is presented. The proposed design framework deals with the generation and programming of memristor crossbar configuration that incorporates the desirable quantum circuit, leading to a technology agnostic design tool. To such a degree, various quantum gates can be efficiently emulated on memristor crossbar configurations for various types of memristive devices, aiming to assist and accelerate the fabrication process of a memristor based quantum simulator.

The time series of the brain are usually characterized by the co-existence of synchronized and desynchronized behaviors. This kind of behavior is related to normal and disorderly functions of the brain. One of the suggested mechanisms to understand thoroughly this behavior are chimera states, which are characterized by the coincidence of coherent and incoherent dynamics that can be exploited through networks of symmetrically coupled identical oscillators. In this work, ring-based networks of Chua's circuits, the simplest electronic oscillators that perform chaotic and well-known bifurcation phenomena, have been extensively studied in memristive crossbars (Xbar), revealing various collective spatio-temporal behaviors, such as chimera states. With respect to different Xbar connectivities and via SPICE-level circuit simulations, the proposed Xbar system proves its efficacy to reproduce spatio-temporal patterns spanning from complete synchronization and chimera states up to fully chaotic states.

Research progress in edge computing hardware, capable of demanding in-the-field processing tasks with simultaneous memory and low power properties, is leading the way towards a revolution in IoT hardware technology. Resistive random access memories (RRAM) are promising candidates for replacing current non-volatile memories and realize storage class memories, but also due to their memristive nature they are the perfect candidates for in-memory computing architectures. In this context, a CMOS compatible silicon nitride (SiN) device with memristive properties is presented accompanied by a data-fitted model extracted through analysis of measured resistance switching dynamics. Additionally, a new phototransistor-based image sensor architecture with integrated SiN memristor (1P1R) was presented. The in-memory computing capabilities of the 1P1R device were evaluated through SPICE-level circuit simulation with the previous presented device model. Finally, the fabrication aspects of the sensor are discussed.

Memristor networks are capable of low-power and massive parallel processing and information storage. Moreover, they have presented the ability to apply for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. Beyond the memory and conventional computing architectures, memristors are widely studied in circuits aiming for increased intelligence that are suitable to tackle complex problems in a power and area efficient manner, offering viable solutions oftenly arriving also from the biological principles of living organisms. In this paper, a memristive circuit exploiting the dynamics of oscillating networks is utilized for the resolution of very popular and NP-complete logic puzzles, like the well-known “Sudoku”. More specifically, the proposed circuit design methodology allows for appropriate usage of interconnections' advantages in a oscillation network and of memristor's switching dynamics resulting to logic-solvable puzzle-instances. The reduced complexity of the proposed circuit and its increased scalability constitute its main advantage against previous approaches and the broadly presented SPICE based simulations provide a clear proof of concept of the aforementioned appealing characteristics.

Since CMOS technology approaches its physical limits, the spotlight of computing technologies and architectures shifts to unconventional computing approaches. In this area, novel computing systems, inspired by natural and mostly nonelectronic approaches, provide also new ways of performing a wide range of computations, from simple logic gates to solving computationally hard problems. Reaction-diffusion processes constitute an information processing method, occurs in nature and are capable of massive parallel and low-power computing, such as chemical computing through Belousov-Zhabotinsky reaction. In this paper, inspired by these chemical processes and based on the wave-propagation information processing taking place in the reaction-diffusion media, the novel characteristics of the nanoelectronic element memristor are utilized to design innovative circuits of electronic excitable medium to perform both classical (Boolean) calculations and to model neuromorphic computations in the same Memristor-RLC (M-RLC) reconfigurable network.

Cellular Automata (CA) is a nature-inspired and widespread computational model which is based on the collective and emergent parallel computing capability of units (cells) locally interconnected in an abstract brain-like structure. Each such unit, referred as CA cell, performs simplistic computations/processes. However, a network of such identical cells can exhibit nonlinear behavior and be used to model highly complex physical phenomena and processes and to solve problems that are highly complicated for conventional computers. Brain activity has always been considered one of the most complex physical processes and its modeling is of utter importance. This work combines the CA parallel computing capability with the nonlinear dynamics of the memristor, aiming to model brain activity during the epileptic seizures caused by the spreading of pathological dynamics from focal to healthy brain regions. A CA-based confrontation extended to include long-range interactions, combined with the recent notion of memristive electronics, is thus proposed as a modern and promising parallel approach to modeling of such complex physical phenomena. Simulation results show the efficiency of the proposed design and the appropriate reproduction of the spreading of an epileptic seizure.