  Present day computers expend orders of magnitude more computational resources
to perform various cognitive and perception related tasks that humans routinely
perform everyday. This has recently resulted in a seismic shift in the field of
computation where research efforts are being directed to develop a
neurocomputer that attempts to mimic the human brain by nanoelectronic
components and thereby harness its efficiency in recognition problems. Bridging
the gap between neuroscience and nanoelectronics, this paper attempts to
provide a review of the recent developments in the field of spintronic device
based neuromorphic computing. Description of various spin-transfer torque
mechanisms that can be potentially utilized for realizing device structures
mimicking neural and synaptic functionalities is provided. A cross-layer
perspective extending from the device to the circuit and system level is
presented to envision the design of an All-Spin neuromorphic processor enabled
with on-chip learning functionalities. Device-circuit-algorithm co-simulation
framework calibrated to experimental results suggest that such All-Spin
neuromorphic systems can potentially achieve almost two orders of magnitude
energy improvement in comparison to state-of-the-art CMOS implementations.
