Abstract
Recently, a system of spintronic vortex oscillators has been experimentally trained to classify vowel sounds. In this paper, we have carried out a combination of device-level and system-level simulations to train a system of spin Hall nano oscillators (SHNOs) of smaller size (25X lower in area compared to those vortex oscillators) for such data classification tasks. Magnetic moments precess in an uniform mode as opposed to the vortex mode in our oscillators. We have trained our system to classify inputs in various popular machine learning data sets like Fisher’s Iris data set of flowers, Wisconsin Breast Cancer (WBC) data set, and MNIST data set of handwritten digits. We have employed a new technique for input dimensionality reduction here so that the clustering/target synchronization pattern changes based on the nature of the data in the different data sets. Our demonstration of learning in a system of such small SHNOs for a wide range of data sets is promising for scaling up the oscillator-based neuromorphic system for complex data classification tasks.
Highlights
We model such uniformmode spin Hall nano oscillators (SHNOs) through micromagnetics and show that they can be locked to the external radio frequency (RF) magnetic field, which mimic the input values in reduced dimensions much like the vortex mode oscillators in Romera et al
We show learning on several popular machine learning data sets like Fisher’s Iris data set of flowers,17 Wisconsin Breast Cancer (WBC) data set,18 and the MNIST data set of handwritten digits19 (Sec. 1 of supplementary material)
In this paper, we have shown that a system of very small SHNOs (75 nm in diameter), where the moments precess in a nearly uniform mode, can be used to classify inputs in different popular machine learning data sets
Summary
Spintronics-based neuromorphic/in-memory computing hardware has been considered to be a low energy alternative to transistorbased digital hardware, which has memory-computing separation, for data classification tasks. One approach for spintronic neuromorphic computing is designing an analog crossbar array of domain-wall-based or skyrmion-based synaptic devices. Fully Connected Neural Network (FCNN) algorithms, of spiking or nonspiking type, are implemented on this crossbar. The properties/parameters of the activation function, or neuron, are static in this case. In an alternative approach, Romera et al. use the dynamic properties of synchronized spintronic oscillators in an experimentally fabricated network with far less parameters, which need to be tuned, to achieve on-chip learning for classification of seven vowel sounds. Romera et al. propose that in order to scale up the system for more complicated classification tasks and to reduce the power consumption, a spin torque oscillator of much lower diameter needs to be used.. Romera et al. propose that in order to scale up the system for more complicated classification tasks and to reduce the power consumption, a spin torque oscillator of much lower diameter needs to be used.13,14 At this lower length scale, the nanomagnet may not oscillate in the vortex mode but rather oscillate close to the uniform mode. Most individual magnetic moments inside the ferromagnetic layer of the spin torque oscillator always remain parallel to each other and precess in unison about an axis. So it is important that considering this uniform mode of oscillation, classification is shown for a system of such coupled spin torque oscillators
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