Using software and hardware neural networks in a Higgs search

Lindblad, Th.; Lindsey, Clark S.; Block, F.; Jayakumar, A.

doi:10.1016/0168-9002(94)01247-4

Cited by 8 publications

(2 citation statements)

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“…Since the very beginning of the neural network field, analog implementations have accompanied theoretical developments sometimes with amazingly short delays. Analog chips implementing neural networks have been also shown to be very effective in many practical applications such as intelligent control, 42,43 nuclear physics, [12][13][14]39,124 optical character recognition, 106 tracking and target recognition (the Irvine Sensors-JPL chip mentioned in Sec. 2.2), etc.…”

Section: Discussionmentioning

confidence: 99%

Neural Networks in Analog Hardware — Design and Implementation Issues

Drăghici

2000

Int. J. Neur. Syst.

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This paper presents a brief review of some analog hardware implementations of neural networks. Several criteria for the classification of general neural networks implementations are discussed and a taxonomy induced by these criteria is presented. The paper also discusses some characteristics of analog implementations as well as some trade-offs and issues identified in the work reviewed. Parameters such as precision, chip area, power consumption, speed and noise susceptibility are discussed in the context of neural implementations. A unified review of various "VLSI friendly" algorithms is also presented. The paper concludes with some conclusions drawn from the analysis of the implementations presented.

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Section: Discussionmentioning

confidence: 99%

Neural Networks in Analog Hardware — Design and Implementation Issues

Drăghici

2000

Int. J. Neur. Syst.

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“…Other gradient descent-based optimization methods have also been implemented on neuromorphic systems for training, and they tend to be variations of back-propagation that have been adapted or simplified in some way [639], [645], [709], [716], [718], [719], [723], [792], [812], [844], [1030], [1122], [1300]- [1303]. Back-propagation methods have also been developed in chip-in-the-loop training methods [686], [702], [732], [815], [859]; in this case, most of the learning takes place on a host machine or off-chip, but the evaluation of the solution network is done on the chip. These methods can help to take into account some of the device's characteristics, such as component variation.…”

Section: A Supervised Learningmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

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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.

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