IMSE-CNM

Oscillatory Neural Networks (ONNs) exhibit a high potential for energy-efficient computing. In ONNs, neurons are implemented with oscillators and synapses with resistive and/or capacitive coupling between pairs of oscillators. Computing is carried out on the basis of the rich, complex, nonlinear synchronization dynamics of a system of coupled oscillators. The exploited synchronization phenomena in ONNs are an example of fully parallel collective computing.A fast system´s convergence to stable states, which correspond to the desired processed information, enables an energy-efficient solution if small area and low-power oscillators are used, specifically, when they are built on the basis of the hysteresis exhibited by phase-transition materials such as VO2. In recent years, there have been numerous studies on ONNs using VO2. Most of them report simulation results. Although in some cases experimental results are also shown, they don´t implement the design techniques that other works on electrical simulations report that allow to improve the behavior of the ONNs.Experimental validation of these approaches is necessary. Therefore, in this work, we describe an ONN realized in a commercial CMOS technology in which the oscillators are built using a circuit that we have developed to emulate the VO2 device. The purpose is to be able to study in depth the synchronization dynamics of relaxation oscillators similar to those that can be performed with VO2 devices. The fabricated circuit is very flexible. It allows programming the synapses to implement different ONNs, calibrating the frequency of the oscillators or controlling their initialization. It uses differential oscillators and resistive synapses equivalent to the use of memristors. In this article, the designed and fabricated circuit is described in detail and experimental results are shown. Specifically, its satisfactory operation as an associative memory is demonstrated. The experiments carried out allow us to conclude that the ONN must be operated according to the type of computational task to be solved, and guidelines are extracted in this regard.

Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO2 constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich nonlinear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network, hence Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. An extensive amount of literature is available about learning in Hopfield networks, with information regarding many different learning algorithms that perform better than the Hebbian rule. However, not all of these algorithms are useful for ONN training due to the constraints imposed by their physical implementation. This paper evaluates different learning methods with respect to their suitability for ONNs. It proposes a new approach, which is compared against previous works. The proposed method has been shown to produce competitive results in terms of pattern recognition accuracy with reduced precision in synaptic weights, and to be suitable for online learning.

Abstract not available

Oscillatory Neural Network (ONN) is a promising neuromorphic computing approach which uses networks of frequency-locked coupled oscillators, and their inherent parallel synchronization to compute. Also, ONN can be im-plemented using phase-transition materials using nano-scale area, low voltage amplitude and reduced power consumption, being an efficient way to im-plement oscillator-based computing. In state-of-theart, ONN is built with a fully-connected architecture, with coupling configured depending on the tar-get application. Its most widespread use has been as associative memory, but recently it is gathering interest as a solver for non-deterministic polynomial time problem (NP-hard). This is performed on the basis of encoding the NP-problem in the Ising model, so ONN operates as an Ising machine. ONN state naturally evolves to minimum points in the Hamiltonian energy function re-sorting to its rich non-lineal dynamics, supposing a promising paradigm of fast, low-power, parallel computation.