  A promising branch of neuromorphic computing aims to perform cognitive
operations in hardware leveraging the physics of efficient and well-established
nano-devices. In this work, we present a reconfigurable classifier based on a
network of electrically connected magnetic tunnel junctions that categorizes
information encoded in the amplitude of input currents through the spin torque
driven magnetization switching output configuration. The network can be trained
to classify new data by adjusting additional programming currents applied
selectively to the junctions. We experimentally demonstrate that a network
composed of three magnetic tunnel junctions can learn to classify spoken vowels
with a recognition rate that surpasses the performance of software multilayer
neural networks with the same number of trained parameters in this task. These
results, obtained with the same nano-devices and working principle employed in
industrial spin-transfer torque magnetic random-access memories (STT-MRAM),
constitute an important step towards the development of large-scale
neuromorphic networks based on well-established technology.
