Hardware Implementation of a Real-Time Neural Network Controller Set for Reactive Power Compensation Systems

Bayındır, Ramazan; Gorgun, Alper

doi:10.1109/icmla.2010.107

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Control tasks have been popular for neuromorphic systems because they typically require real-time performance, are often deployed in real systems that require small volume and low power, and have a temporal processing component, so they benefit from models that utilize recurrent connections or delays on synapses. A large variety of different control applications have utilized neuromorphic systems [466], [687], [688], [863], [872], [895], [902], [903], [920], [932], [962], [1017], [1051], [1085], [1660], [2561], [2562], but by far the most common control test case is the cart-pole problem or the inverted pendulum task [487], [512], [792], [902], [903], [1008], [1045], [1337], [1340], [2563]. Neuromorphic systems have also been applied to video games, such as Pong [1563], PACMAN [2405], and Flappy Bird [1337].…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

173

180

View full text Add to dashboard Cite

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.

show abstract