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Review of algorithms for artificial intelligence on low memory devices
<span title="">2021</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
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This triggered a rebranding of neural nets into 'deep learning' [30] and new solutions emerged based on training a Restricted Boltzmann Machine (RBM) efficiently [31] . ...
Concept: Review the basic fundamentals of AIe.g., 'The Boltzmann Machine', 'The Helmholtz Machine', The Elman network -and combine with state-of-the-art deep learning algorithms to discover novel methods ...
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Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks
[article]
<span title="2020-02-03">2020</span>
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arXiv
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Boltzmann Machines (RBMs). ...
Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing devices are garnering increasing interest as a means to compactly and efficiently realize machine learning operations in Restricted ...
ACKNOWLEDGMENTS This work was supported in part by the Center for Probabilistic Spin Logic for Low-Energy Boolean and Non-Boolean Computing (CAPSL), one of the Nanoelectronic Computing Research (nCORE) ...
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A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers
[article]
<span title="2017-07-13">2017</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2017-03-15">2017</span>
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to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. ...
achieve optimal results, and 3) the implementation of neuron model is expensive in both cost and power. ...
Department of Energy ...
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Research topic: neuromorphic engineering systems and applications. A snapshot of neuromorphic systems engineering
<span title="2014-12-19">2014</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a>
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They demonstrate that a DBN constructed from stacks of restricted Boltzmann machines (RBMs) is valuable for learning and computing sensor fusion. ...
Two papers are on the hot topic (based on largest number of views) of event-driven computation in deep belief networks (DBNs) (O'Connor et al., 2013; Neftci et al., 2014) . ...
and no conflict of interest exists. ...
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A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition
<span title="2016-11-01">2016</span>
<i title="Institute of Advanced Engineering and Science">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hdpiucmugffofjsjdrgay6346e" style="color: black;">APTIKOM Journal on Computer Science and Information Technologies</a>
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Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. ...
It is also known as deep structured learning, hierarchical learning or deep machine learning. The term "deep learning" indicates the method used in training multi-layered neural networks. ...
Deep Belief Networks (DBN) Deep belief networks [2] are highly complex directed acyclic graph, which are formed by a sequence of restricted Boltzmann Machine (RBM) architectures. ...
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A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition
<span title="2020-01-17">2020</span>
<i title="Pandawan">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hdpiucmugffofjsjdrgay6346e" style="color: black;">APTIKOM Journal on Computer Science and Information Technologies</a>
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Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), DeepBelief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. ...
It is also known as deep structured learning,hierarchical learning or deep machine learning. The term "deep learning" indicates the method used in trainingmulti-layered neural networks. ...
Deep Belief Networks (DBN) Deep belief networks [2] are highly complex directed acyclic graph, which are formed by a sequence of restricted Boltzmann Machine (RBM) architectures. ...
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Adiabatic Quantum Computation Applied to Deep Learning Networks
<span title="2018-05-18">2018</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a>
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We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum ...
for training deep learning networks. ...
, such as spiking Hopfield networks and spiking restricted Boltzmann machines [43] . ...
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The Latest Progress of Deep Learning
深度学习的最新进展
<span title="">2018</span>
<i title="Hans Publishers">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/az52o5fkdnbcxn2sr2gndxelp4" style="color: black;">Computer Science and Application</a>
</i>
深度学习的最新进展
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This paper introduces the related concepts of deep learning, concise summary of the relevant work based on the relevant academic achievements obtained from domestic and overseas searches, 19 classic papers ...
and the latest three books on the depth of the past three years. ...
在 Acoustic modeling using deep belief networks [6在 Efficient learning of sparse representations with an energy-based model [在 Real-time classification and sensor fusion with a spiking deep belief network ...
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Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform
<span title="">2018</span>
<i title="EDP Sciences">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4vlgvitw6fcmbay5hkyo2s2ime" style="color: black;">MATEC Web of Conferences</a>
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Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. ...
Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence ...
study and the discussion with the SpiNNaker team as well as with the experts in the field of neuromorphic technology. ...
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Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines
<span title="2019-02-12">2019</span>
<i title="Springer Nature">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a>
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Restricted Boltzmann Machines (RBM), for their local learning rule and inherent tolerance to stochasticity, comply with both of these constraints and constitute a highly attractive algorithm towards achieving ...
One of the biggest stakes in nanoelectronics today is to meet the needs of Artificial Intelligence by designing hardware neural networks which, by fusing computation and memory, process and learn from ...
Acknowledgements This work was supported by the European Research Council Starting Grant NANOINFER (reference: 715872) and the Agence Nationale de la Recherche Grant MIRA (reference: ANR-15-CE24-023). ...
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Event-driven contrastive divergence for spiking neuromorphic systems
<span title="">2014</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a>
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Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification ...
Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time ...
ACKNOWLEDGMENTS This work was partially funded by the National Science Foundation (NSF EFRI-1137279, CCF-1317560), the Office of Frontiers in Neuroscience | Neuromorphic Engineering January 2014 | Volume ...
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A Survey of Neuromorphic Computing and Neural Networks in Hardware
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<span title="2017-05-19">2017</span>
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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 ...
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 ...
An extremely common use of both neural networks and neuromorphic implementations has been on various image-based applications, including edge detection [220] , [339] , [520] , [783] , [829] , [922 ...
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SNRA: A Spintronic Neuromorphic Reconfigurable Array for In-Circuit Training and Evaluation of Deep Belief Networks
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<span title="2019-01-08">2019</span>
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evaluation phases of restricted Boltzmann machines (RBMs). ...
First, probabilistic spin logic devices are used to develop an RBM realization which is adapted to construct deep belief networks (DBNs) having one to three hidden layers of size 10 to 800 neurons each ...
ACKNOWLEDGMENT This work was supported in part by the Center for Probabilistic Spin Logic for Low-Energy Boolean and Non-Boolean Computing (CAPSL), one of the Nanoelectronic Computing Research (nCORE) ...
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Real-time classification and sensor fusion with a spiking deep belief network
<span title="">2013</span>
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Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. ...
Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. ...
Instead of speculating on Moore's FIGURE 1 | Boltzmann and Restricted Boltzmann Machines. ...
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Deep Learning With Spiking Neurons: Opportunities and Challenges
<span title="2018-10-25">2018</span>
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This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. ...
Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific ...
AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. ...
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