22 research outputs found

    Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs

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    Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input

    Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs

    Get PDF
    Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO 3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input

    2022 roadmap on neuromorphic computing and engineering

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    Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018^{18} calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

    Tunnel junction based memristors as artificial synapses

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    Thomas A, Niehörster S, Fabretti S, et al. Tunnel junction based memristors as artificial synapses. Frontiers in Neuroscience. 2015;9: 241.We prepared magnesia, tantalum oxide and barium titanate based junction structures and investigated their memristive properties. The low amplitudes of the resistance change in these types of junctions are the major obstacle for their use. Here, we increased the amplitude of the resistance change from 10% up to 100%. Utilizing the memristive properties, we looked into the use of the junction structures as artificial synapses. We observed analogs of longterm potentiation, long-term depression and spike-time dependent plasticity in these simple two terminal devices. Finally, we suggest a possible pathway of these devices towards their integration in neuromorphic systems for storing analog synaptic weights and supporting the implementation of biologically plausible learning mechanisms

    Molecular dynamics simulations of nanoclusters in neuromorphic systems

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    Neuromorphic computing is a new computing paradigm that deals with computing tasks using inter-connected artificial neurons inspired by the natural neurons in the human brain. This computational architecture is more efficient in performing many complex tasks such a pattern recognition and has promise at overcoming some of the limitations of conventional computers. Among the emerging types of artificial neurons, a cluster-based neuromorphic device is a promising system with good costefficiency because of a simple fabrication process. This device functions using the formation and breakage of the connections (“synapses”) between clusters, driven by the bias voltage applied to the clusters. The mechanisms of the formation and breakage of these connections are thus of the utmost interest. In this thesis, the molecular dynamics simulation method is used to explore the mechanisms of the formation and breakage of the connections (“filaments”) between the clusters in a model of neuromorphic device. First, the Joule heating mechanism of filament breakage is explored using a model consisting of Au nanowire that connects two Au1415 clusters. Upon heating, the atoms of the nanofilament gradually aggregate towards the clusters, causing the middle of the wire to graduallythin and then suddenly break. Most of the system remains crystalline during this process, but the centre becomes molten. The terminal clusters increase the melting point of the nanowires by fixing them and act as recrystallisation regions. A strong dependence of the breaking temperature is found not only on the width of the nanowires but also their length and atomic structure. Secondly, the bridge formation and thermal breaking processes between Au1415 clusters on a graphite substrate are also simulated. The bridging process , which can heal a broken filament, is driven by diffusion of gold along the graphite substrate. The characteristic times of bridge formation are explored at elevated simulation temperatures to estimate the longer characteristic times of formation at room-temperature conditions. The width of the bridge formed has a power-law dependence on the simulation time, and the mechanism is a combination of diffusion and viscous flow. Simulations of bridgebreaking are also conducted and reveal the existence of a voltage threshold that must be reached to activate the breakage. The role of the substrate in the bridge formation and breakage processes is revealed as a medium of diffusion. Lastly, to explore future potential cluster materials, the thermal behaviour of Pb-Al core-shell clusters is studied. The core and shell are found to melt separately. In fact, the core atoms of nanoclusters tend to escape their shells and partially cover them, leading to a preference for a segregated state. The melting point of the core can either be depressed or elevated, depending on the thickness of the shell due to different mechanisms

    Chalcogenide and metal-oxide memristive devices for advanced neuromorphic computing

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    Energy-intensive artificial intelligence (AI) is prevailing and changing the world, which requires energy-efficient computing technology. However, traditional AI driven by von Neumann computing systems suffers from the penalties of high-energy consumption and time delay due to frequent data shuttling. To tackle the issue, brain-inspired neuromorphic computing that performs data processing in memory is developed, reducing energy consumption and processing time. Particularly, some advanced neuromorphic systems perceive environmental variations and internalize sensory signals for localized in-senor computing. This methodology can further improve data processing efficiency and develop multifunctional AI products. Memristive devices are one of the promising candidates for neuromorphic systems due to their non-volatility, small size, fast speed, low-energy consumption, etc. In this thesis, memristive devices based on chalcogenide and metal-oxide materials are fabricated for neuromorphic computing systems. Firstly, a versatile memristive device (Ag/CuInSe2/Mo) is demonstrated based on filamentary switching. Non-volatile and volatile features are coexistent, which play multiple roles of non-volatile memory, selectors, artificial neurons, and artificial synapses. The conductive filaments’ lifetime was controlled to present both volatile and non-volatile behaviours. Secondly, the sensing functions (temperature and humidity) are explored based on Ag conductive filaments. An intelligent matter (Ag/Cu(In, Ga)Se2/Mo) endowing reconfigurable temperature and humidity sensations is developed for sensory neuromorphic systems. The device reversibly switches between two states with differentiable semiconductive and metallic features, demonstrating different responses to temperature and humidity variations. Integrated devices can be employed for intelligent electronic skin and in-sensor computing. Thirdly, the memristive-based sensing function of light was investigated. An optoelectronic synapse (ITO/ZnO/MoO3/Mo) enabling multi-spectrum sensitivity for machine vision systems is developed. For the first time, this optoelectronic synapse is practical for front-end retinomorphic image sensing, convolution processing, and back-end neuromorphic computing. This thesis will benefit the development of advanced neuromorphic systems pushing forward AI technology
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