Real-Time Face Tracking and Recognition on IBM Neuromorphic Chip

Chevitarese, Daniel Salles; Santos, Marcelo Nery Dos

doi:10.1109/ism.2016.0142

Cited by 3 publications

(2 citation statements)

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“…Spiking models have been popular in neuromorphic implementations in part because of their event-drive nature and improved energy efficiency relative to other systems. As such, implementations of other neural network models have been created using spiking neuromorphic systems, including spiking feed-forward networks [571]- [575], spiking recurrent networks [576]- [581], spiking deep neural networks [582]- [592], spiking deep belief networks [593], spiking Hebbian systems [594]- [602], spiking Hopfield networks or associative memories [603]- [605], spiking winner-take-all networks [606]- [611], spiking probabilistic networks [612], [613], and spiking random neural networks [614]. In these implementations a spiking neural network architecture in neuromorphic systems has been utilized for another neural network model type.…”

Section: Network Modelsmentioning

confidence: 99%

“…Other image classification tasks that have been demonstrated on neuromorphic systems include classifying real world images such as traffic signs [1018], [1065], [1305], face recognition or detection [582], [1067], [1204], [1284], [1885], [2130], [2585], [2591], [2615], car recognition or detection [1883], detecting air pollution in images [2616], detection of manufacturing defects or defaults [1692], hand gesture recognition [558], [1038], human recognition [1422], object texture analysis [2617], and other real world image recognition tasks [825], [1037], [1058], [2127]. There are several common real-world image data sets that have been evaluated on neuromorphic systems, including the CalTech-101 data set [867], [1068], the Google Street-View House Number (SVHN) data set [316], [2580], [2585], [2590], [2591], the CIFAR10 data set [587], [592], [1066], [1069], [2130], [2590], [2591], and ImageNet [1827].…”

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

173

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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