Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators

Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input voltage. Here we show that the frequency and the phase of the oscillator can also be used to recognize waveforms. For this purpose, we phase-lock the oscillator to the input waveform, which carries information in its modulated frequency. In this way we considerably decrease amplitude, phase and frequency noise. We show that this method allows classifying sine and square waveforms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features

Role of non-linear data processing on speech recognition task in the framework of reservoir computing

The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition. However, this task requires acoustic transformations from sound waveforms with varying amplitudes to frequency domain maps that can be seen as feature extraction techniques. Depending on the conversion method, these may obscure the contribution of the neuromorphic hardware to the overall speech recognition performance. Here, we quantify and separate the contributions of the acoustic transformations and the neuromorphic hardware to the speech recognition success rate. We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate benchmark for comparing different hardware. Finally, we experimentally and numerically quantify the impact of the different acoustic transformations for neuromorphic hardware based on magnetic nano-oscillators.Comment: 13 pages, 5 figure

Magnetoresistive Sensor in Two-Dimension on a 25 μm Thick Silicon Substrate for In Vivo Neuronal Measurements

International audienceNeuronal electrical activity is widely studied in vivo, and the ability to measure its magnetic equivalent to obtain an undisturbed signal with both amplitude and direction information leading to neuronal signal mapping would be a promising tool for neuroscience. To provide such a tool, a probe with spin-electronics-based magnetic sensors with orthogonal axes of sensitivity for two directions of measurement is realized, thanks to a local magnetization re-orientation technique induced by Joule heating. This probe is tested under in vivo measurement conditions in the brain of an anesthetized rat. To be as close as possible to neurons and to create minimal damage during the probe’s insertion, the tip thickness has been drastically decreased using a silicon-on-insulator substrate. Our probes provide the ability to perform in vivo magnetic measurements on two orthogonal axes on a 25 μm thick silicon tip with a sensitivity of 1.7%/mT along one axis and 0.9%/mT along the perpendicular axis in the sensor plane, for a limit of detection at 1 kHz of 1.0 and 1.3 nT, respectively. These probes have been tested through a phantom study and during an in vivo experiment. The robustness and stability over one year are demonstrated

Multiple Giant-Magnetoresistance Sensors Controlled by Additive Dipolar Coupling

Vertical packaging of multiple giant magnetoresistance (multi-GMR) stacks is a very interesting noise reduction strategy for local magnetic sensor measurements, which has not been reported experimentally so far. Here, we fabricate multi-GMR sensors (up to 12 repetitions) that maintain a good GMR ratio, linearity, and low roughness. From magnetotransport measurements, two different resistance responses are observed with a crossover at around five GMR repetitions: steplike (N < 5) and linear (N ≥ 5) behavior, respectively. With the help of micromagnetic simulations, we analyze, in detail, the two main magnetic mechanisms: the Néel coupling distribution induced by the roughness propagation and the additive dipolar coupling between the N free layers. Furthermore, we correlate the dipolar coupling mechanism, which is controlled by the number of GMRs (N) and lateral dimensions (width), to the sensor performance (sensitivity, noise, and detectivity); this is in good agreement with analytical theory. The noise roughly decreases in multi-GMRs as 1/√N in both regimes (low-frequency 1/f and thermal noise). The sensitivity is even more strongly reduced, scaling as N^−1, in the strong dipolar regime (narrow devices), while converging to a constant value in the weak dipolar regime (wide devices). Interestingly, they are more robust against undesirable random telegraphic noise than single GMRs at high voltages, and the linearity can be extended towards a much larger magnetic field range, without dealing with the size and reduction of the GMR ratio. Finally, we identify the optimal conditions for which multi-GMRs exhibit lower magnetic field detectivity than that of single GMRs: wide devices operating in the thermal regime, where much higher voltage can be applied without generating remarkable magnetic noise. These results open the path towards spintronics sensors connected and coupled in three dimensions with reduced noise, compact footprint, and mainly tuned by the dipolar coupling

Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

Deep learning has an increasing impact to assist research, allowing, for example, the discovery of novel materials. Until now, however, these artificial intelligence techniques have fallen short of discovering the full differential equation of an experimental physical system. Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them. For this purpose, we re-frame the formalism of Neural Ordinary Differential Equations to the constraints of spintronics: few measured outputs, multiple inputs and internal parameters. We demonstrate with Neural Ordinary Differential Equations an accel- eration factor over 200 compared to micromagnetic simulations for a complex problem – the simulation of a reservoir computer made of magnetic skyrmions (20 minutes compared to three days). In a second realization, we show that we can predict the noisy response of experimental spintronic nano-oscillators to varying inputs after training Neural Ordinary Differential Equations on five milliseconds of their measured response to a different set of inputs. Neural Ordinary Differential Equations can therefore constitute a disruptive tool for developing spintronic applications in complement to micromagnetic simulations, which are time-consuming and cannot fit experiments when noise or imperfections are present. Our approach can also be generalized to other electronic devices involving dynamics

Brain-Inspired Computing with Spintronics Devices

Neural networks and artificial intelligence utilizing artificial neurons and synapses are attracting much attention. Spintronic devices are considered to be suitable for mimicking artificial synapses and artificial neurons because of nonvolatility of information and rich nonlinearity of spin dynamics. We focused on the nonlinearity of spin dynamics and formed a virtual artificial neural network by using the time multiplexing method. By using reservoir computing for learning rules, we succeeded in speech recognition with a high recognition rate of 99.6%. These results pave the way for hardware implementation of artificial intelligence