Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems

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.

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