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 brainlike 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. We begin with 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 as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation in a way that is inspired by concepts learned about the human brain. SNNs are artificial networks made up of neurons that fire a pulse, or spike, once the accumulated value of the inputs to the neuron exceeds a threshold. One of the most challenging parts of designing neuromorphic hardware is handling the vast degree of connectivity that neurons have with each other in the form of synaptic connections. This paper analyzes the neuromorphic systems Neurogrid, Braindrop, SpiNNaker, BrainScaleS, TrueNorth, Loihi, Darwin, and Dynap-SEL; and discusses the design of large scale spiking communication networks used in such systems. In particular, this paper looks at how each of these systems solved the challenges of forming packets with spiking information and how these packets are routed within the system. The routing of packets is analyzed at two scales: How the packets should be routed when traveling a short distance, and how the packets should be routed over longer global connections. Additional topics, such as the use of asynchronous circuits, robustness in communication, connection with a host machine, and network synchronization are also covered.INDEX TERMS Communication protocols, field programmable gate arrays, interconnect, network on chip, neuromorphic, spiking network communication, spiking neural network, very large scale integration.Surely there must be a less primitive way of making big changes in the store than by pushing vast numbers of words back and forth through the von Neumann bottleneck. Not only is this tube a literal bottleneck for the data traffic of a problem, but, more importantly, it is an intellectual bottleneck that has kept us tied to wordat-a-time thinking instead of encouraging us to think in terms of the larger conceptual units of the task at hand. Thus programming is basically planning and detailing the enormous traffic of words through the von Neumann bottleneck, and much of that traffic concerns not significant data itself but where to find it.

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