  Brain-inspired computing - leveraging neuroscientific principles underpinning
the unparalleled efficiency of the brain in solving cognitive tasks - is
emerging to be a promising pathway to solve several algorithmic and
computational challenges faced by deep learning today. Nonetheless, current
research in neuromorphic computing is driven by our well-developed notions of
running deep learning algorithms on computing platforms that perform
deterministic operations. In this article, we argue that taking a different
route of performing temporal information encoding in probabilistic neuromorphic
systems may help solve some of the current challenges in the field. The article
considers superparamagnetic tunnel junctions as a potential pathway to enable a
new generation of brain-inspired computing that combines the facets and
associated advantages of two complementary insights from computational
neuroscience -- how information is encoded and how computing occurs in the
brain. Hardware-algorithm co-design analysis demonstrates $97.41\%$ accuracy of
a state-compressed 3-layer spintronics enabled stochastic spiking network on
the MNIST dataset with high spiking sparsity due to temporal information
encoding.
