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Present day computers expend orders of magnitude more computational resourcesto perform various cognitive and perception related tasks that humans routinelyperform everyday. This has recently resulted in a seismic shift in the field ofcomputation where research efforts are being directed to develop aneurocomputer that attempts to mimic the human brain by nanoelectroniccomponents and thereby harness its efficiency in recognition problems. Bridgingthe gap between neuroscience and nanoelectronics, this paper attempts toprovide a review of the recent developments in the field of spintronic devicebased neuromorphic computing. Description of various spin-transfer torquemechanisms that can be potentially utilized for realizing device structuresmimicking neural and synaptic functionalities is provided. A cross-layerperspective extending from the device to the circuit and system level ispresented to envision the design of an All-Spin neuromorphic processor enabledwith on-chip learning functionalities. Device-circuit-algorithm co-simulationframework calibrated to experimental results suggest that such All-Spinneuromorphic systems can potentially achieve almost two orders of magnitudeenergy improvement in comparison to state-of-the-art CMOS implementations.

Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic Computing