Memristor Crossbar for Adaptive Synchronization

Gambuzza, Lucia Valentina; Frasca, Mattia; Fortuna, Luigi; Ntinas, Vasileios; Vourkas, Ioannis; Sirakoulis, Georgios Ch.

doi:10.1109/tcsi.2017.2692519

Cited by 35 publications

(21 citation statements)

References 40 publications

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“…From a dynamical system perspective, CC consists of three state variables related to voltages V 1 , V 2 across the capacitors C 1 , C 2 , and the current i L through the inductor L. The coupling between two CCs is realised by the interconnection of their state variables, most commonly by a resistive or capacitive coupling element between their V 1 nodes. In previous works, the adaptivity of memristor's conductivity was utilised for the adaptive coupling of CC networks that leads to full coherence [20]. Other forms of system synchronization have been presented with non-adaptive coupling [16], [17] in ring-based networks where the coupling parameters govern the system synchronization regime.…”

Section: Coupling Chua's Circuitsmentioning

confidence: 99%

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Emergence of Chimera States with Re-Programmable Memristor Crossbar Arrays

Tsakalos

Ntinas

Karamani

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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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.

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Section: Coupling Chua's Circuitsmentioning

confidence: 99%

“…, which corresponds to uncoupled CCs. The values of R min , R max , as well as the switching behaviour of memristor, are described by the voltage-controlled memristor model in [33], while the memristor parameter set is adopted from [20].…”

Section: Coupling Chua's Circuitsmentioning

confidence: 99%

Emergence of Chimera States with Re-Programmable Memristor Crossbar Arrays

Tsakalos

Ntinas

Karamani

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

Self Cite

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“…The most important property of MC is that it can be programed by involving the three basic operations of read, write and adapt . Read and write operations can be performed by three modes: current control, voltage control, and spiking control .…”

Section: Neuromorphic Memristor Crossbarmentioning

confidence: 99%

Neuromorphic Computing with Memristor Crossbar

Zhang

Huang

et al. 2018

Physica Status Solidi (a)

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Neural networks, one of the key artificial intelligence technologies today, have the computational power and learning ability similar to the brain. However, implementation of neural networks based on the CMOS von Neumann computing systems suffers from the communication bottleneck restricted by the bus bandwidth and memory wall resulting from CMOS downscaling. Consequently, applications based on large-scale neural networks are energy/ area hungry and neuromorphic computing systems are proposed for efficient implementation of neural networks. Neuromorphic computing system consists of the synaptic device, neuronal circuit, and neuromorphic architecture. With the two-terminal nonvolatile nanoscale memristor as the synaptic device and crossbar as parallel architecture, memristor crossbars are proposed as a promising candidate for neuromorphic computing. Herein, neuromorphic computing systems with memristor crossbars are reviewed. The feasibility and applicability of memristor crossbars based neuromorphic computing for the implementation of artificial neural networks and spiking neural networks are discussed and the prospects and challenges are also described.

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“…The emerging technology of resistive switching devices (memristors) [1] has attracted considerable attention from academia and industry owing to its potential impact in different domains of electronics [2] - [4]. In this context, the overall response of memristors, when they are organized into networks, gained an ever-increasing interest after Pershin and Di Ventra demonstrated that this could lead to a new analog computing approach which they named "memcomputing" [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Shortest Path Computing in Directed Graphs with Weighted Edges Mapped on Random Networks of Memristors

Fernandez

Vourkas

Rubio

2020

Parallel Process. Lett.

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To accelerate the execution of advanced computing tasks, in-memory computing with resistive memory provides a promising solution. In this context, networks of memristors could be used as parallel computing medium for the solution of complex optimization problems. Lately, the solution of the shortest-path problem (SPP) in a two-dimensional memristive grid has been given wide consideration. Some still open problems in such computing approach concern the time required for the grid to reach to a steady state, and the time required to read the result, stored in the state of a subset of memristors that represent the solution. This paper presents a circuit simulation-based performance assessment of memristor networks as SPP solvers. A previous methodology was extended to support weighted directed graphs. We tried memristor device models with fundamentally different switching behavior to check their suitability for such applications and the impact on the timely detection of the solution. Furthermore, the requirement of binary vs. analog operation of memristors was evaluated. Finally, the memristor network-based computing approach was compared to known algorithmic solutions to the SPP over a large set of random graphs of different sizes and topologies. Our results contribute to the proper development of bio-inspired memristor network-based SPP solvers.

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Memristor Crossbar for Adaptive Synchronization

Cited by 35 publications

References 40 publications

Emergence of Chimera States with Re-Programmable Memristor Crossbar Arrays

Emergence of Chimera States with Re-Programmable Memristor Crossbar Arrays

Neuromorphic Computing with Memristor Crossbar

Shortest Path Computing in Directed Graphs with Weighted Edges Mapped on Random Networks of Memristors

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