  The use of analog resistance states for storing weights in neuromorphic
systems is impeded by fabrication imprecision and device stochasticity that
limit the precision of synapse weights. This challenge can be resolved by
emulating analog behavior with the stochastic switching of the binary states of
spin-transfer torque magnetoresistive random-access memory (STT-MRAM). However,
previous approaches based on STT-MRAM operate in an asynchronous manner that is
difficult to implement experimentally. This paper proposes a synchronous
spiking neural network system with clocked circuits that perform unsupervised
learning leveraging the stochastic switching of STT-MRAM. The proposed system
enables a single-layer network to achieve 90% inference accuracy on the MNIST
dataset.
