A Cascadable Neural Network Chip Set With On-chip Learning Using Noise And Gain Annealing

Jayakumar, A.; Alspector, Joshua

doi:10.1109/cicc.1992.591335

Cited by 10 publications

(4 citation statements)

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“…A special case of stochastic neural networks, Boltzmann machines, have also been popular in neuromorphic systems. The general Boltzmann machine was utilized in neuromorphic systems primarily in the early 1990's [12], [1193]- [1199], but it has seen occasional implementations in more recent publications [1200]- [1203]. A more common use of the Boltzmann model is the restricted Boltzmann machine, because the training time is significantly reduced when compared with a general Boltzmann machine.…”

Section: Network Modelsmentioning

confidence: 99%

“…These approaches include the least-mean-squares algorithm [750], [787], [1025], [1026], weight perturbation [19], [625], [655], [669], [682], [698], [699], [708], [710], [712], [713], [715], [736], [834], [835], [841], [845]- [847], [856], [1078]- [1080], [1098], [1099], [1148], [1304], training specifically for convolutional neural networks [1305], [1306] and others [169], [220], [465], [714], [804], [864], [865], [1029], [1049], [1307]- [1320]. Other on-chip supervised learning mechanisms are built for particular model types, such as Boltzmann machines, restricted Boltzmann machines, or deep belief networks [12], [627], [1135], [1193], …”

Section: A Supervised Learningmentioning

confidence: 99%

“…In the early years of development, the two spirals problem was a common benchmark [1315], [2641], [2642], [2649], [2650]. N -bit parity tasks have also been commonly used [209], [217], [625], [657]- [659], [661], [680], [696], [697], [737], [855], [1092], [1193], [1346], [1349], [1883]- [1885], [2640], [2650], [2651]. Basic function approximation has also been a common task for neuromorphic systems, where a mathematical function is specified and the neuromorphic system is trained to replicate its output behavior [196], [419]- [421], [514], [518], [519], [614], [628], [643], [715], [819], [873], [874], [971], [983], [984], [1024], [1053], [1078], [1079], [1098],…”

Section: Applicationsmentioning

confidence: 99%

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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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Section: Network Modelsmentioning

confidence: 99%

Section: A Supervised Learningmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

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show abstract

“…6,97,98 The board can contain up to six neural network chips. The chips support meanfield annealing and Boltzmann learning for on-chip weight changes.…”

Section: Neural Network Implementations In Analog Hardwarementioning

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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Paralleled Hardware Annealing for Optimal Solutions

Sheu

Choi

1995

Neural Information Processing and VLSI

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A Cascadable Neural Network Chip Set With On-chip Learning Using Noise And Gain Annealing

Cited by 10 publications

References 3 publications

A Survey of Neuromorphic Computing and Neural Networks in Hardware

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Neural Networks in Analog Hardware — Design and Implementation Issues

Paralleled Hardware Annealing for Optimal Solutions

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