  We propose a new network architecture for standard spin-Hall magnetic tunnel
junction-based spintronic neurons that allows them to compute multiple critical
convolutional neural network functionalities simultaneously and in parallel,
saving space and time. An approximation to the Rectified Linear Unit transfer
function and the local pooling function are computed simultaneously with the
convolution operation itself. A proof-of-concept simulation is performed on the
MNIST dataset, achieving up to 98% accuracy at a cost of less than 1 nJ for all
convolution, activation and pooling operations combined. The simulations are
remarkably robust to thermal noise, performing well even with very small
magnetic layers.
