Shuai Li

Beijing University of Chinese Medicine

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R. G. Elliman

Australian National University

Xinjun Liu

Tianjin University

Sanjoy Kumar Nandi

Australian National University

Shimul Kanti Nath

UNSW Sydney

V. Dinesh

Lala Lajpat Rai University of Veterinary and Animal Sciences

Julie Grollier

Centre National de la Recherche Scientifique

Damien Querlioz

Centre de Nanosciences et de Nanotechnologies

Yeheng Bo

Tianjin University

Sébastien Gérard

Res Publica (Norway)

Juan Song

Chongqing Medical University

Cooperative Institutions

Université Paris-Saclay

Centre National de la Recherche Scientifique

Laboratoire Albert Fert

Beijing University of Chinese Medicine

Huazhong University of Science and Technology

Wuhan City Chinese Medicine Hospital

Tianjin University

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

Thales (France)

Université Paris-Sud

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From User Stories to Models: A Machine Learning Empowered Automation

Takwa Kochbati Shuai Li Sébastien Gérard Chokri Mraidha

In modern software development, manually deriving architecture models from software requirements expressed in natural language becomes a tedious and time-consuming task particularly for more complex systems.Moreover, the increase in size of the developed systems raises the need to decompose the software system into sub-systems at early stages since such decomposition aids to better design the system architecture.In this paper, we propose a machine learning based approach to automatically break-down the system into sub-systems and generate preliminary architecture models from natural language user stories in the Scrum process.Our approach consists of three pillars.Firstly, we compute word level similarity of requirements using word2vec as a prediction model.Secondly, we extend it to the requirement level similarity computation, using a scoring formula.Thirdly, we employ the Hierarchical Agglomerative Clustering algorithm to group the semantically similar requirements and provide an early decomposition of the system.Finally, we implement a set of specific Natural Language Processing heuristics in order to extract relevant elements that are needed to build models from the identified clusters.Ultimately, we illustrate our approach by the generation of sub-systems expressed as UML use-case models and demonstrate its applicability using three case studies.

Heuristics

10.5220/0010197800280040

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Citations (18)

EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

iScience (2021)

Erwann Martin Maxence Ernoult Jérémie Laydevant Shuai Li Damien Querlioz

Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology.

Neuromorphic engineering

MNIST database

Backpropagation

Implementation

10.1016/j.isci.2021.102222

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Citations (46)

Anatomy of filamentary threshold switching in amorphous niobium oxide

Nanotechnology (2018)

Shuai Li Xinjun Liu Sanjoy Kumar Nandi R. G. Elliman

The threshold switching behavior of Pt/NbO x /TiN devices is investigated as a function device area and NbO x film thickness and shown to reveal important insight into the structure of the self-assembled switching region. The devices exhibit combined selector-memory (1S1R) behavior after an initial voltage-controlled forming process, but exhibit symmetric threshold switching when the RESET and SET currents are kept below a critical value. In this mode, the threshold and hold voltages are independent of the device area and film thickness but the threshold current (power), while independent of device area, decreases with increasing film thickness. These results are shown to be consistent with a structure in which the threshold switching volume is confined, both laterally and vertically, to the region between the residual memory filament and the TiN electrode, and where the memory filament has a core-shell structure comprising a metallic core and a semiconducting shell. The veracity of this structure is demonstrated by comparing experimental results with the predictions of a simple circuit model, and more detailed finite element simulations. These results provide further insight into the structure and operation of NbO x threshold switching devices that have application in emerging memory and neuromorphic computing fields.

Niobium oxide

Natural bond orbital

Neuromorphic engineering

10.1088/1361-6528/aacee4

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Citations (38)

Understanding composite negative differential resistance in niobium oxide memristors

Journal of Physics D Applied Physics (2021)

Xinjun Liu Peng Zhang Shimul Kanti Nath Shuai Li Sanjoy Kumar Nandi

Abstract Volatile memristors, or threshold switching devices, exhibit a diverse range of negative differential resistance (NDR) characteristics under current-controlled operation and understanding the origin of these responses is of great importance for exploring their potential as nano-scale oscillators for neuromorphic computing. Here we use a previously developed two-zone, parallel memristor model to undertake a systematic analysis of NDR modes in two-terminal metal-oxide-metal devices. The model assumes that the non-uniform current distribution associated with filamentary conduction can be represented by a high current density core and a lower current-density shell where the core is assumed to have a memristive response due to Poole-Frenkel conduction and the shell is represented by either a fixed resistor or a second memristive region. A detailed analysis of the electrical circuits is undertaken using a lumped-element thermal model of the core-shell structure, and is shown to reproduce continuous and discontinuous NDR responses, as well as more complex compound behaviour. Finally, an interesting double-window oscillation behaviour is predicted and experimentally verified for a device with compound NDR behaviour. These results clearly identify the origin of different NDR responses and provide a strong basis for designing devices with complex NDR characteristics.

Memristor

Neuromorphic engineering

Oscillation (cell signaling)

Electrical element

10.1088/1361-6463/ac3bf4

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Set-based anomaly detection and interpolating predictive regulation of UPQC for power distribution quality improvement

2022 China International Conference on Electricity Distribution (CICED) (2022)

Hejin Liu Guo-Qiang Su Yang Liu Shuai Li Xinhong You

This paper investigates the monitoring and predictive control of unified power quality conditioner to improve power quality in a power distribution system. The proposed strategy is composed of a set-based anomaly monitoring method and an interpolating predictive regulation method. The novelty of such strategy consists of two aspects. On the one hand, the set-based anomaly monitoring scheme features a user-defined output observation set given to characterize the different severity levels of harmonic effects that the user can tolerate. On the other hand, a linear constrained linear programming control method based on interpolation optimization is designed to achieve fast predictive regulation of unified power quality conditioner. Simulation studies on a single-phase power distribution system are given to validate that the proposed strategy is able to guarantee the quality of power distribution, whether the supply voltage/load current has harmonics or sags/swells problems.

Model Predictive Control

Voltage Sag

Interpolation

10.1109/ciced56215.2022.9928997

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Schottky-Barrier-Induced Asymmetry in the Negative-Differential-Resistance Response of Nb/NbOx / Pt Cross-Point Devices

Physical Review Applied (2020)

Shimul Kanti Nath Sanjoy Kumar Nandi Assaad El Helou Xinjun Liu Shuai Li

The negative-differential-resistance (NDR) response of $\mathrm{Nb}$/${\mathrm{Nb}\mathrm{O}}_{x}$/$\mathrm{Pt}$ cross-point devices is shown to have a polarity dependence due to the effect of the metal-oxide Schottky barriers on the contact resistance. Three distinct responses are observed under opposite polarity testing: bipolar S-type NDR, bipolar snapback NDR, and combined S-type and snapback NDR, depending on the stoichiometry of the oxide film and device area. In situ thermoreflectance imaging is used to show that these NDR responses are associated with strong current localization, thereby justifying the use of a previously developed two-zone, core-shell thermal model of the device. The observed polarity-dependent NDR responses, and their dependence on stoichiometry and area, are then explained by extending this model to include the effect of the polarity-dependent contact resistance. This study provides an improved understanding of the NDR response of metal-oxide-metal structures and informs the engineering of devices for neuromorphic computing and nonvolatile memory applications.

Polarity (international relations)

Snapback

Stoichiometry

Neuromorphic engineering

10.1103/physrevapplied.13.064024

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Citations (15)

Design and application of automatic inspection system for smart distribution station based on cloud-edge collaboration

International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022) (2022)

Hejin Liu Guoqiang Su Min Huang Shuai Li Pengping Zhang

In order to solve the problem of online inspection of distribution station operation and maintenance, a design method of automatic inspection system for smart distribution station based on cloud-edge collaboration is proposed. Firstly, the overall architecture of the smart distribution station automatic inspection system based on cloud-edge collaboration is designed. Then, the system software architecture is designed in detail through three major parts and 12 functions including cloud resource layer, system platform layer and application layer. Secondly, the hardware architecture of the smart station terminal is designed in detail through the construction of the secure access area and the configuration of the virtual server of the cloud master station. Thirdly, based on artificial intelligence speech recognition technology, the overall architecture of the voice inspection assistant is described. Finally, the software development and application show that there is important practical significance in improving the intelligent maintenance level of distribution network.

10.1117/12.2659849

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Forming-free bipolar resistive switching and quantum conductance in NiO/FTO structures

Journal of Physics D Applied Physics (2019)

Ling Wei Shuai Li Sanjoy Kumar Nandi R. G. Elliman

Forming-free bipolar resistive switching and quantum conductance effects are reported for Au/NiO/FTO structures. The devices show multistep conductance changes during the RESET process for temperatures in the range from 295 to 373 K. Statistical histograms based on 650 switching cycles indicates that the conductivity changes are well represented by integer or half integer multiples of the quantum conductance G0. Temperature dependent I-V measurements below the switching voltages can be described using a Poole-Frenkel model. The mechanism of forming-free bipolar switching and quantum conductance effect in the Au/NiO/FTO structure is interpreted by multiple quasi-filaments existing in as-grown NiO films and the numbers for the multiple conducting filaments decrease gradually in RESET processes when they rupture gradually.

Non-blocking I/O

Resistive touchscreen

10.1088/1361-6463/ab3aa4

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Citations (2)

Threshold switching and electrical self-oscillation in niobium oxide films

Journal of Applied Physics (2016)

Xinjun Liu Shuai Li Sanjoy Kumar Nandi V. Dinesh R. G. Elliman

Electrical self-sustained oscillations have been observed in a broad range of two-terminal systems and are of interest as possible building blocks for bio-inspired neuromorphic computing. In this work, we experimentally explore voltage-controlled oscillations in NbOx devices with a particular focus on understanding how the frequency and waveform are influenced by circuit parameters. We also introduce a finite element model of the device based on a Joule-heating induced insulator-metal transition. The electroformed device structure is represented by a cylindrical conductive channel (filament) comprised of NbO/NbO2 zones and surrounded by an Nb2O5−x matrix. The model is shown to reproduce the current-controlled negative differential resistance observed in measured current-voltage curves, and is combined with circuit elements to simulate the waveforms and dynamics of an isolated Pearson–Anson oscillator. Such modeling is shown to provide considerable insight into the relationship between the material response and device and circuit characteristics.

Electroforming

Oscillation (cell signaling)

Memristor

10.1063/1.4963288

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Citations (78)

Characterization and modeling of spiking and bursting in experimental NbOx neuron

arXiv (Cornell University) (2022)

Marie Drouhin Shuai Li Matthieu Grelier Sophie Collin Florian Godel

Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these devices show limited neuronal behaviors and have to be integrated in more complex circuits to implement the rich dynamics of biological neurons. Here we studied a NbOx memristor neuron that is capable of emulating numerous neuronal dynamics, including tonic spiking, stochastic spiking, leaky-integrate-and-fire features, spike latency, temporal integration. The device also exhibits phasic bursting, a property that has scarcely been observed and studied in solid-state nano-neurons. We show that we can reproduce and understand this particular response through simulations using non-linear dynamics. These results show that a single NbOx device is sufficient to emulate a collection of rich neuronal dynamics that paves a path forward for realizing scalable and energy-efficient neuromorphic computing paradigms.

Neuromorphic engineering

Memristor

Bursting

10.48550/arxiv.2209.00413

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