A Survey of Neuromorphic Computing and Neural Networks in Hardware

TLDR

Neuromorphic computing refers to brain‑inspired computers, devices, and models that contrast von Neumann architecture, enabling highly connected synthetic neurons and synapses for neuroscience modeling and machine learning, but face significant challenges in accurate brain modeling, material and engineering breakthroughs, programming frameworks, and application development. This survey reviews the history, motivations, and major research areas of neuromorphic computing, and discusses future research directions needed to realize its promise. The authors conduct a 35‑year review of neuromorphic computing, covering neuro‑inspired models, algorithms, hardware, supporting systems, and applications, to provide an exhaustive overview and highlight gaps that motivate future research.

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. 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.