  Topologically protected spin textures, such as magnetic skyrmions, have the
potential for dense data storage as well as energy-efficient computing due to
their small size and a low driving current. The evaluation of the writing and
reading of the skyrmion's magnetic and electrical characteristics is a key step
toward the implementation of these devices. In this paper, we present the
magnetic heterostructure Hall bar device and study the anomalous Hall and
topological Hall signals in the device. Using the combination of different
measurements like magnetometry at different temperatures, Hall effect
measurement from 2K to 300K, and magnetic force microscopy imaging, we
investigate the magnetic and electrical characteristics of the magnetic
structure. We measure the skyrmion topological resistivity at different
temperatures as a function of the magnetic field. The topological resistivity
is maximum around the zero magnetic field and it decreases to zero at the
saturating field. This is further supported by MFM imaging. Interestingly the
resistivity decreases linearly with the field, matching the behavior observed
in the corresponding micromagnetic simulations. We combine the experimental
results with micromagnetic simulations, thus propose a skyrmion-based synaptic
device and show spin-orbit torque-controlled potentiation/depression in the
device. The device performance as the synapse for neuromorphic computing is
further evaluated in a convolutional neural network CNN. The neural network is
trained and tested on the MNIST data set we show devices acting as synapses
achieving a recognition accuracy close to 90%, on par with the ideal
software-based weights which offer an accuracy of 92%.
