Memristive excitable cellular automata

The memristor is a device whose resistance changes depending on the polarity and magnitude of a voltage applied to the device's terminals. We design a minimalistic model of a regular network of memristors using structurally-dynamic cellular automata. Each cell gets info about states of its closest neighbours via incoming links. A link can be one 'conductive' or 'non-conductive' states. States of every link are updated depending on states of cells the link connects. Every cell of a memristive automaton takes three states: resting, excited (analog of positive polarity) and refractory (analog of negative polarity). A cell updates its state depending on states of its closest neighbours which are connected to the cell via 'conductive' links. We study behaviour of memristive automata in response to point-wise and spatially extended perturbations, structure of localised excitations coupled with topological defects, interfacial mobile excitations and growth of information pathways.Comment: Accepted to Int J Bifurcation and Chaos (2011

Memristive cellular automata for modeling of epileptic brain activity

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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.Peer ReviewedPostprint (author's final draft

Phenomenology of retained refractoriness: On semi-memristive discrete media

We study two-dimensional cellular automata, each cell takes three states: resting, excited and refractory. A resting cell excites if number of excited neighbours lies in a certain interval (excitation interval). An excited cell become refractory independently on states of its neighbours. A refractory cell returns to a resting state only if the number of excited neighbours belong to recovery interval. The model is an excitable cellular automaton abstraction of a spatially extended semi-memristive medium where a cell's resting state symbolises low-resistance and refractory state high-resistance. The medium is semi-memristive because only transition from high- to low-resistance is controlled by density of local excitation. We present phenomenological classification of the automata behaviour for all possible excitation intervals and recovery intervals. We describe eleven classes of cellular automata with retained refractoriness based on criteria of space-filling ratio, morphological and generative diversity, and types of travelling localisations

MemCA: all-memristor design for deterministic and probabilistic cellular automata hardware realization

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksInspired by the behavior of natural systems, Cellular Automata (CA) tackle the demanding long-distance information transfer of conventional computers by the massive parallel computation performed by a set of locally-coupled dynamical nodes. Although CA are envisioned as powerful deterministic computers, their intrinsic capabilities are expanded after the memristor’s probabilistic switching is introduced into CA cells, resulting in new hybrid deterministic and probabilistic memristor-based CA (MemCA). In the proposed MemCA hardware realization, memristor devices are incorporated in both the cell and rule modules, composing the very first all-memristor CA hardware, designed with mixed CMOS/Memristor circuits. The proposed implementation accomplishes high operating speed and reduced area requirements, exploiting also memristor as an entropy source in every CA cell. MemCA’s functioning is showcased in deterministic and probabilistic operation, which can be externally modified by the selection of programming voltage amplitude, without changing the design. Also, the proposed MemCA system includes a reconfigurable rule module implementation that allows for spatial and temporal rule inhomogeneity.Peer ReviewedPostprint (published version

Memristive Learning Cellular Automata: Theory and Applications

Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place.Their nanoscale dimensions and low power consumption enable the further design of various nanoelectronic processing circuits and corresponding computing architectures, like neuromorhpic, in memory, unconventional, etc.One of the possible ways to exploit the memristor's advantages is by combining them with Cellular Automata (CA).CA constitute a well known non von Neumann computing architecture that is based on the local interconnection of simple identical cells forming N-dimensional grids.These local interconnections allow the emergence of global and complex phenomena.In this paper, we propose a hybridization of the CA original definition coupled with memristor based implementation, and, more specifically, we focus on Memristive Learning Cellular Automata (MLCA), which have the ability of learning using also simple identical interconnected cells and taking advantage of the memristor devices inherent variability.The proposed MLCA circuit level implementation is applied on optimal detection of edges in image processing through a series of SPICE simulations, proving its robustness and efficacy

Memristive Perceptron for Combinational Logic Classification

The resistance of the memristor depends upon the past history of the input current or voltage; so it can function as synapse in neural networks. In this paper, a novel perceptron combined with the memristor is proposed to implement the combinational logic classification. The relationship between the memristive conductance change and the synapse weight update is deduced, and the memristive perceptron model and its synaptic weight update rule are explored. The feasibility of the novel memristive perceptron for implementing the combinational logic classification (NAND, NOR, XOR, and NXOR) is confirmed by MATLAB simulation

Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation

We undertook a study of the use of a memristor network for music generation, making use of the memristor's memory to go beyond the Markov hypothesis. Seed transition matrices are created and populated using memristor equations, and which are shown to generate musical melodies and change in style over time as a result of feedback into the transition matrix. The spiking properties of simple memristor networks are demonstrated and discussed with reference to applications of music making. The limitations of simulating composing memristor networks in von Neumann hardware is discussed and a hardware solution based on physical memristor properties is presented.Comment: 22 pages, 13 pages, conference pape

Cellular Nonlinear Networks: optimized implementation on FPGA and applications to robotics

L'objectiu principal d'aquesta tesi consisteix a estudiar la factibilitat d'implementar un sensor càmera CNN amb plena funcionalitat basat en FPGA de baix cost adequat per a aplicacions en robots mòbils. L'estudi dels fonaments de les xarxes cel•lulars no lineals (CNNs) i la seva aplicació eficaç en matrius de portes programables (FPGAs) s'ha complementat, d'una banda amb el paral•lelisme que s'estableix entre arquitectura multi-nucli de les CNNs i els eixams de robots mòbils, i per l'altre banda amb la correlació dinàmica de CNNs i arquitectures memristive. A més, els memristors es consideren els substituts dels futurs dispositius de memòria flash per la seva capacitat d'integració d'alta densitat i el seu consum d'energia prop de zero. En el nostre cas, hem estat interessats en el desenvolupament d’FPGAs que han deixat de ser simples dispositius per a la creació ràpida de prototips ASIC per esdevenir complets dispositius reconfigurables amb integració de la memòria i els elements de processament general. En particular, s'han explorat com les arquitectures implementades CNN en FPGAs poden ser optimitzades en termes d’àrea ocupada en el dispositiu i el seu consum de potència. El nostre objectiu final ens ah portat a implementar de manera eficient una CNN-UM amb complet funcionament a un baix cost i baix consum sobre una FPGA amb tecnología flash. Per tant, futurs estudis sobre l’arquitectura eficient de la CNN sobre la FPGA i la interconnexió amb els robots comercials disponibles és un dels objectius d'aquesta tesi que se seguiran en les línies de futur exposades en aquest treball.El objetivo principal de esta tesis consiste en estudiar la factibilidad de implementar un sensor cámara CNN con plena funcionalidad basado en FPGA de bajo coste adecuado para aplicaciones en robots móviles. El estudio de los fundamentos de las redes celulares no lineales (CNNs) y su aplicación eficaz en matrices de puertas programables (FPGAs) se ha complementado, por un lado con el paralelismo que se establece entre arquitectura multi -núcleo de las CNNs y los enjambres de robots móviles, y por el otro lado con la correlación dinámica de CNNs y arquitecturas memristive. Además, los memristors se consideran los sustitutos de los futuros dispositivos de memoria flash por su capacidad de integración de alta densidad y su consumo de energía cerca de cero. En nuestro caso, hemos estado interesados en el desarrollo de FPGAs que han dejado de ser simples dispositivos para la creación rápida de prototipos ASIC para convertirse en completos dispositivos reconfigurables con integración de la memoria y los elementos de procesamiento general. En particular, se han explorado como las arquitecturas implementadas CNN en FPGAs pueden ser optimizadas en términos de área ocupada en el dispositivo y su consumo de potencia. Nuestro objetivo final nos ah llevado a implementar de manera eficiente una CNN-UM con completo funcionamiento a un bajo coste y bajo consumo sobre una FPGA con tecnología flash. Por lo tanto, futuros estudios sobre la arquitectura eficiente de la CNN sobre la FPGA y la interconexión con los robots comerciales disponibles es uno de los objetivos de esta tesis que se seguirán en las líneas de futuro expuestas en este trabajo.The main goal of this thesis consists in studying the feasibility to implement a full-functionality CNN camera sensor based on low-cost FPGA device suitable for mobile robotic applications. The study of Cellular Nonlinear Networks (CNNs) fundamentals and its efficient implementation on Field Programmable Gate Arrays (FPGAs) has been complemented, on one side with the parallelism established between multi-core CNN architecture and swarm of mobile robots, and on the other side with the dynamics correlation of CNNs and memristive architectures. Furthermore, memristors are considered the future substitutes of flash memory devices because of its capability of high density integration and its close to zero power consumption. In our case, we have been interested in the development of FPGAs that have ceased to be simple devices for ASIC fast prototyping to become complete reconfigurable devices embedding memory and processing elements. In particular, we have explored how the CNN architectures implemented on FPGAs can be optimized in terms of area occupied on the device or power consumption. Our final accomplishment has been implementing efficiently a fully functional reconfigurable CNN-UM on a low-cost low-power FPGA based on flash technology. Therefore, further studies on an efficient CNN architecture on FPGA and interfacing it with commercially-available robots is one of the objectives of this thesis that will be followed in the future directions exposed in this work