A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices

In this paper we present a biorealistic model for the first part of the early vision processing by incorporating memristive nanodevices. The architecture of the proposed network is based on the organisation and functioning of the outer plexiform layer (OPL) in the vertebrate retina. We demonstrate that memristive devices are indeed a valuable building block for neuromorphic architectures, as their highly non-linear and adaptive response could be exploited for establishing ultra-dense networks with similar dynamics to their biological counterparts. We particularly show that hexagonal memristive grids can be employed for faithfully emulating the smoothing-effect occurring at the OPL for enhancing the dynamic range of the system. In addition, we employ a memristor-based thresholding scheme for detecting the edges of grayscale images, while the proposed system is also evaluated for its adaptation and fault tolerance capacity against different light or noise conditions as well as distinct device yields

Memristors

This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

Simulating Memristive Networks in SystemC-AMS

This chapter presents a solution for the simulation of large memristive networks with SystemC-AMS. SystemC-AMS allows simulating memristors both on analogue level and on digital level to link analogue memristive devices to digital circuits and system level specifications. We investigate the benefits and drawbacks of a SystemC-AMS simulation compared to a simulation in SPICE. We show for the example of a two-layer memristive network emulating an optical flow algorithm by the detection of moving edges that large memristive networks can be simulated with a free available SystemC-AMS simulation environment, whereas free available SPICE simulation environment fails. However, it is also shown that commercial SPICE simulators are superior against current SystemC-AMS implementations concerning the size of simulated memristive networks. However, SystemC-AMS simulations of memristive networks offer both still more flexibility and similar run times compared to commercial SPICE simulators for small-sized memristive networks. The flexibility and the powerfulness of a SystemC-AMS solution is demonstrated for a complex network that solves edge detection, filtering and detecting of moving objects. The possible run times of the memristive network are determined in the SystemC-AMS simulation environment and are compared with an optical flow algorithm on classical hardware like a CPU and a GPU

Spin-Based Neuron Model with Domain Wall Magnets as Synapse

We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design

Shortest path computing in directed graphs with weighted edges mapped on random networks of memristors

Electronic version of an article published as [Fernandez, Carlos, Ioannis Vourkas, and Antonio Rubio. "Shortest Path Computing in Directed Graphs with Weighted Edges Mapped on Random Networks of Memristors." Parallel Processing Letters 30.01 (2020): 2050002] [https://doi.org/10.1142/S0129626420500024] © [copyright World Scientific Publishing Company] [https://www.worldscientific.com/worldscinet/ppl]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.This work was supported by the Chilean research grants CONICYT REDES ETAPA INICIAL Convocatoria 2017 No. REDI170604, CONICYT BASAL FB0008, and by the Spanish MINECO and ERDF (TEC2016-75151-C3-2-R).Peer ReviewedPostprint (author's final draft

A memristive non-smooth dynamical system with coexistence of bimodule periodic oscillation

© 2022 Elsevier GmbH. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.aeue.2022.154279In order to explore the bursting oscillations and the formation mechanism of memristive non-smooth systems, a third-order memristor model and an external periodic excitation are introduced into a non-smooth dynamical system, and a novel 4D memristive non-smooth system with two-timescale is established. The system is divided into two different subsystems by a non-smooth interface, which can be used to simulate the scenario where a memristor encounters a non-smooth circuit in practical application circuits. Three different bursting patterns and bifurcation mechanisms are analyzed with the time series, the corresponding phase portraits, the equilibrium bifurcation diagrams, and the transformed phase portraits. It is pointed that not only the stability of the equilibrium trajectory but also the non-smooth interface may influence the bursting phenomenon, resulting in the sudden jumping of the trajectory and non-smooth bifurcation at the non-smooth interface. In particular, the coexistence of bimodule periodic oscillations at the non-smooth interface can be observed in this system. Finally, the correctness of the theoretical analysis is well verified by the numerical simulation and Multisim circuit simulation. This paper is of great significance for the future analysis and engineering application of the memristor in non-smooth circuits.Peer reviewe