  Neuromorphic computing aims to mimic both the function and structure of
biological neural networks to provide artificial intelligence with extreme
efficiency. Conventional approaches store synaptic weights in non-volatile
memory devices with analog resistance states, permitting in-memory computation
of neural network operations while avoiding the costs associated with
transferring synaptic weights from a memory array. However, the use of analog
resistance states for storing weights in neuromorphic systems is impeded by
stochastic writing, weights drifting over time through stochastic processes,
and limited endurance that reduces the precision of synapse weights. Here we
propose and experimentally demonstrate neuromorphic networks that provide
high-accuracy inference thanks to the binary resistance states of magnetic
tunnel junctions (MTJs), while leveraging the analog nature of their stochastic
spin-transfer torque (STT) switching for unsupervised Hebbian learning. We
performed the first experimental demonstration of a neuromorphic network
directly implemented with MTJ synapses, for both inference and
spike-timing-dependent plasticity learning. We also demonstrated through
simulation that the proposed system for unsupervised Hebbian learning with
stochastic STT-MTJ synapses can achieve competitive accuracies for MNIST
handwritten digit recognition. By appropriately applying neuromorphic
principles through hardware-aware design, the proposed STT-MTJ neuromorphic
learning networks provide a pathway toward artificial intelligence hardware
that learns autonomously with extreme efficiency.
