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Neuromorphic computing has come to refer to a variety of brain-inspiredcomputers, devices, and models that contrast the pervasive von Neumann computerarchitecture. This biologically inspired approach has created highly connectedsynthetic neurons and synapses that can be used to model neuroscience theoriesas well as solve challenging machine learning problems. The promise of thetechnology is to create a brain-like ability to learn and adapt, but thetechnical challenges are significant, starting with an accurate neurosciencemodel of how the brain works, to finding materials and engineeringbreakthroughs to build devices to support these models, to creating aprogramming framework so the systems can learn, to creating applications withbrain-like capabilities. In this work, we provide a comprehensive survey of theresearch and motivations for neuromorphic computing over its history. We beginwith a 35-year review of the motivations and drivers of neuromorphic computing,then look at the major research areas of the field, which we define asneuro-inspired models, algorithms and learning approaches, hardware anddevices, supporting systems, and finally applications. We conclude with a broaddiscussion on the major research topics that need to be addressed in the comingyears to see the promise of neuromorphic computing fulfilled. The goals of thiswork are to provide an exhaustive review of the research conducted inneuromorphic computing since the inception of the term, and to motivate furtherwork by illuminating gaps in the field where new research is needed.

A Survey of Neuromorphic Computing and Neural Networks in Hardware