Microwave neural processing and broadcasting with spintronic nano-oscillators

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

Synaptic Weight Modulation and Logic Function Learning with Electro-grafted Nano Organic Memristors

International audienceNeuromorphic computing has gained important attention since it is an efficient way to handle advanced cognitive tasks such as image recognition and classification. Hardware implementation of an artificial neural network (ANN) requires arrays of scalable memory elements to act as artificial synapses. Memristors, which are two-terminal analog memory devices, are excellent candidates for this application as their tuna-ble resistance could be used to code and store synaptic weights with, in principle, low power consumption. In this work, we studied metal-organic-metal memristors in which the organic layer is a dense and robust electro-grafted thin film of redox complexes. The process allows fabricating planar and vertical junctions, as well as small crossbar arrays. The unipolar devices display non-volatile multi-level conductivity states with high RMAX/RMIN ratio and two distinct thresholds. The characteristics of individual memristors were characterized in depth with respect to the targeted synaptic function. We notably showed that they possess the Spike Timing-Dependent Plasticity (STDP) property (their conductivity evolves as a function of the time-delay between incoming pulses at both terminals), which is critical for future applications in neuromorphic circuits based on unsu-pervised learning. In parallel, we implemented a series of memristors as synapses in a simple prototype: a mixed circuit with the neuron implemented with conventional electronics. This ANN is able to learn linearly separable 3-input logic functions through an iterative supervised learning algorithm inspired by the Widrow-Hoff rule

Enhancing the injection locking range of spin torque oscillators through mutual coupling

International audienceWe investigate how the ability of the vortex oscillation mode of a spin-torque nano-oscillator to lock to an external microwave signal is modified when it is coupled to another oscillator. We show experimentally that the mutual electrical coupling can lead to locking range enhancements of a factor 1.64.Furthermore, we analyze the evolution of the locking range as a function of the coupling strength through experiments and numerical simulations. By uncovering the mechanisms at stake in the locking range enhancement, our results will be useful for designing spin-torque nano-oscillator arrays with high sensitivities to external microwave stimul

Vowel recognition with four coupled spin-torque nano-oscillators

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