A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 * 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology.Comment: 26 pages, 24 figure

Method of increasing the information capacity of associative memory of oscillator neural networks using high-order synchronization effect

Computational modelling of two- and three-oscillator schemes with thermally coupled $VO_2$-switches is used to demonstrate a novel method of pattern storage and recognition in an impulse oscillator neural network (ONN) based on the high-order synchronization effect. The method ensures high information capacity of associative memory, i.e. a large number of synchronous states $N_s$. Each state in the system is characterized by the synchronization order determined as the ratio of harmonics number at the common synchronization frequency. The modelling demonstrates attainment of $N_s$ of several orders both for a three-oscillator scheme $N_s$~650 and for a two-oscillator scheme $N_s$~260. A number of regularities are obtained, in particular, an optimal strength of oscillator coupling is revealed when $N_s$ has a maximum. A general tendency toward information capacity decrease is shown when the coupling strength and switch inner noise amplitude increase. An algorithm of pattern storage and test vector recognition is suggested. It is also shown that the coordinate number in each vector should be one less than the switch number to reduce recognition ambiguity. The demonstrated method of associative memory realization is a general one and it may be applied in ONNs with various mechanisms and oscillator coupling topology.Comment: 18 pages, 8 figure

Threshold Switching and Self-Oscillation in Niobium Oxide

Volatile threshold switching, or current controlled negative differential resistance (CC-NDR), has been observed in a range of transition metal oxides. Threshold switching devices exhibit a large non-linear change in electrical conductivity, switching from an insulating to a metallic state under external stimuli. Compact, scalable and low power threshold switching devices are of significant interest for use in existing and emerging technologies, including as a selector element in high-density memory arrays and as solid-state oscillators for hardware-based neuromorphic computing. This thesis explores the threshold switching in amorphous NbOx and the properties of individual and coupled oscillators based on this response. The study begins with an investigation of threshold switching in Pt/NbOx/TiN devices as a function device area, NbOx film thickness and temperature, which provides important insight into the structure of the self-assembled switching region. The devices exhibit combined threshold-memory behaviour 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 shown to be independent of the device area and film thickness, and the threshold power, while independent of device area, is shown to decrease 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 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 resistor network model, and detailed finite element simulations. The next study focuses on electrical self-oscillation of an NbOx threshold switching device incorporated into a Pearson-Anson circuit configuration. Measurements confirm stable operation of the oscillator at source voltages as low as 1.06 V, and demonstrate frequency control in the range from 2.5 to 20.5 MHz with maximum frequency tuning range of 18 MHz/V. The oscillator exhibit three distinct oscillation regimes: sporadic spiking, stable oscillation and damped oscillation. The oscillation frequency, peak-to-peak amplitude and frequency are shown to be temperature and voltage dependent with stable oscillation achieved for temperatures up to ∼380 K. A physics-based threshold switching model with inclusion of device and circuit parameters is shown to explain the oscillation waveform and characteristic. The final study explores the oscillation dynamics of capacitively coupled Nb/Nb2O5 relaxation oscillators. The coupled system exhibits rich collective behaviour, from weak coupling to synchronisation, depending on the negative differential resistance response of the individual devices, the operating voltage and the coupling capacitance. These coupled oscillators are shown to exhibit stable frequency and phase locking states at source voltages as low as 2.2 V with MHz frequency tunable range. The numerical simulation of the coupled system highlights the role of source voltage, and circuit and device capacitance in controlling the coupling modes and dynamics

Hardware Implementation of Differential Oscillatory Neural Networks Using VO 2-Based Oscillators and Memristor-Bridge Circuits

Oscillatory Neural Networks (ONNs) are currently arousing interest in the research community for their potential to implement very fast, ultra-low-power computing tasks by exploiting specific emerging technologies. From the architectural point of view, ONNs are based on the synchronization of oscillatory neurons in cognitive processing, as occurs in the human brain. As emerging technologies, VO2 and memristive devices show promising potential for the efficient implementation of ONNs. Abundant literature is now becoming available pertaining to the study and building of ONNs based on VO2 devices and resistive coupling, such as memristors. One drawback of direct resistive coupling is that physical resistances cannot be negative, but from the architectural and computational perspective this would be a powerful advantage when interconnecting weights in ONNs. Here we solve the problem by proposing a hardware implementation technique based on differential oscillatory neurons for ONNs (DONNs) with VO2-based oscillators and memristor-bridge circuits. Each differential oscillatory neuron is made of a pair of VO2 oscillators operating in anti-phase. This way, the neurons provide a pair of differential output signals in opposite phase. The memristor-bridge circuit is used as an adjustable coupling function that is compatible with differential structures and capable of providing both positive and negative weights. By combining differential oscillatory neurons and memristor-bridge circuits, we propose the hardware implementation of a fully connected differential ONN (DONN) and use it as an associative memory. The standard Hebbian rule is used for training, and the weights are then mapped to the memristor-bridge circuit through a proposed mapping rule. The paper also introduces some functional and hardware specifications to evaluate the design. Evaluation is performed by circuit-level electrical simulations and shows that the retrieval accuracy of the proposed design is comparable to that of classic Hopfield Neural Networks.Unión Europea H2020 grant 871501 “NeurONN,”Spanish Ministry of Economy and Competitivity grant PID2019-105556GB-C31 (NANOMIND) (with support from the European Regional Development Fund)