Sparse neuromorphic computing based on spin-torque diodes

Abstract

We report on the sparse neuromorphic computing based on spin-torque diodes (STDs). The rectification characteristics of STDs have been investigated in the absence and presence of d.c. bias currents. While the injection locking phenomenon is observed in our devices, the output functions versus the d.c. bias currents mimic artificial neurons with sparse representations. Furthermore, we construct a neural network with STD neurons to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database, with a produced accuracy of up to 92.7%. The results suggest that STDs have potential to be building blocks for the realization of a biologically plausible neuromorphic computing system.