A Survey of Neuromorphic Computing and Neural Networks in Hardware
Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas, Birdwell, Mark E. Dean, Garrett S. Rose, and James S. Plank

TL;DR
This comprehensive survey reviews 35 years of neuromorphic computing research, covering models, algorithms, hardware, applications, and future challenges to realize brain-like learning and adaptation in machines.
Contribution
It provides an exhaustive overview of neuromorphic computing history, research areas, and identifies key gaps and future directions for the field.
Findings
Detailed history of neuromorphic research over 35 years
Identification of major research areas and challenges
Discussion of future research priorities
Abstract
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history.…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural Networks and Reservoir Computing
