Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPhase programming in coupled spintronic oscillators
94
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Neurons in the brain behave as a network of coupled nonlinear oscillators processing information by rhythmic activity and interaction. Several technological approaches have been proposed that might enable mimicking the complex information processing of neuromorphic computing, some of them relying on nanoscale oscillators. For example, spin torque oscillators are promising building blocks for the realization of artificial high-density, low-power oscillatory networks (ON) for neuromorphic computing. The local external control and synchronization of the phase relation of oscillatory networks are among the key challenges for implementation with nanotechnologies. Here we propose a new method of phase programming in ONs by manipulation of the saturation magnetization, and consequently the resonance frequency of a single oscillator via Joule heating by a simple DC voltage input. We experimentally demonstrate this method in a pair of stray field coupled magnetic vortex oscillators. Since this method only relies on the oscillatory behavior of coupled oscillators, and the temperature dependence of the saturation magnetization, it allows for variable phase programming in a wide range of geometries and applications that can help advance the efforts of high frequency neuromorphic spintronics up to the GHz regime.
rate research
Read More
Can we build small neuromorphic chips capable of training deep networks with billions of parameters? This challenge requires hardware neurons and synapses with nanometric dimensions, which can be individually tuned, and densely connected. While nanosynaptic devices have been pursued actively in recent years, much less has been done on nanoscale artificial neurons. In this paper, we show that spintronic nano-oscillators are promising to implement analog hardware neurons that can be densely interconnected through electromagnetic signals. We show how spintronic oscillators maps the requirements of artificial neurons. We then show experimentally how an ensemble of four coupled oscillators can learn to classify all twelve American vowels, realizing the most complicated tasks performed by nanoscale neurons.
The emergence of exceptional points (EPs) in the parameter space of a (2D) eigenvalue problem is studied in a general sense in mathematical physics. In coupled systems, it gives rise to unique physical phenomena upon which novel approaches for the development of seminal types of highly sensitive sensors shall leverage their fascinating properties. Here, we demonstrate at room temperature the emergence of EPs in coupled spintronic nano-oscillators, coming along with the observation of amplitude death of self-oscillations and other complex dynamics. The main experimental features are properly described by the linearized theory of coupled system dynamics we develop. Interestingly, these spintronic nanoscale oscillators are deployment-ready in different applicational fields, such as sensors or radiofrequeny and wireless technology and, more recently, novel neuromorphic hardware solutions. Their unique and versatile properties, notably their large nonlinear behavior open up unprecedented perspectives in experiments as well as in theory on the physics of exceptional points.
Chimera states in the systems of nonlocally coupled phase oscillators are considered stable in the continuous limit of spatially distributed oscillators. However, it is reported that in the numerical simulations without taking such limit, chimera states are chaotic transient and finally collapse into the completely synchronous solution. In this paper, we numerically study chimera states by using the coupling function different from the previous studies and obtain the result that chimera states can be stable even without taking the continuous limit, which we call the persistent chimera state.
Chimera states for the one-dimensional array of nonlocally coupled phase oscillators in the continuum limit are assumed to be stationary states in most studies, but a few studies report the existence of breathing chimera states. We focus on multichimera states with two coherent and incoherent regions, and numerically demonstrate that breathing multichimera states, whose global order parameter oscillates temporally, can appear. Moreover, we show that the system exhibits a Hopf bifurcation from a stationary multichimera to a breathing one by the linear stability analysis for the stationary multichimera.
Accumulating evidences show that the cerebral cortex is operating near a critical state featured by power-law size distribution of neural avalanche activities, yet evidence of this critical state in artificial neural networks mimicking the cerebral cortex is lacking. Here we design an artificial neural network of coupled phase oscillators and, by the technique of reservoir computing in machine learning, train it for predicting chaos. It is found that when the machine is properly trained, oscillators in the reservoir are synchronized into clusters whose sizes follow a power-law distribution. This feature, however, is absent when the machine is poorly trained. Additionally, it is found that despite the synchronization degree of the original network, once properly trained, the reservoir network is always developed to the same critical state, exemplifying the attractor nature of this state in machine learning. The generality of the results is verified in different reservoir models and by different target systems, and it is found that the scaling exponent of the distribution is independent on the reservoir details and the bifurcation parameter of the target system, but is modified when the dynamics of the target system is changed to a different type. The findings shed lights on the nature of machine learning, and are helpful to the design of high-performance machine in physical systems.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
