  We propose energy efficient strain control of domain wall (DW) in a
perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate
that can implement multi state synapse to be utilized in neuromorphic computing
platforms. In conjunction with SOT from to a current flowing in the heavy metal
layer, strain is generated by applying a voltage across the piezoelectric. Such
a strain is mechanically transferred to the racetrack and modulates the
Perpendicular Magnetic Anisotropy (PMA). When different voltages are applied
(i.e. different strains are generated), it can translate the DW to different
distances for the same current which implements different synaptic weights. We
have shown using micromagnetic simulations that 5-state and 3-state synapse can
be implemented in a racetrack that is modeled with natural edge roughness and
room temperature thermal noise. Such strain-controlled synapse has an energy
consumption of few fJs and could thus be very attractive to implement
energy-efficient quantized neural networks, which has been shown recently to
achieve near equivalent classification accuracy to the full-precision neural
networks.
