A 1 TOPS/W Analog Deep Machine-Learning Engine With Floating-Gate Storage in 0.13 µm CMOS

Lu, Junjie; Young, Steven R.; Arel, Itamar; Holleman, Jeremy

doi:10.1109/jssc.2014.2356197

Cited by 117 publications

(57 citation statements)

References 22 publications

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“…In addition to the univariate datasets' evaluation, we tested the approaches on 12 Multivariate Time Series (MTS) datasets (Baydogan, 2015). The multivariate evaluation shows another benefit of deep learning models, which is the ability to handle the curse of dimensionality (Bellman, 2010;Keogh and Mueen, 2017) by leveraging different degrees of smoothness in compositional function (Poggio et al, 2017) as well as the parallel computations of the GPUs (Lu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for time series classification: a review

Fawaz

Forestier

Weber

et al. 2019

Data Min Knowl Disc

2,070

712

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Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-ofthe-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

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Section: Introductionmentioning

confidence: 99%

Deep learning for time series classification: a review

Fawaz

Forestier

Weber

et al. 2019

Data Min Knowl Disc

2,070

712

View full text Add to dashboard Cite

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“…The majority of such efforts rely on conventional technology [3] [6], e.g. complimentary metal-oxide-semiconductor (CMOS) circuits to implement artificial neurons and dynamic random access memory [7], static random access memory [8] or non volatile floating memory gate memory [9], [10] to implement artificial synapses. Emerging memory device technologies [11], while not yet mature for large scale implementations, could offer further improvements in artificial neural network performance in the future [12].…”

Section: Introductionmentioning

confidence: 99%

Efficient training algorithms for neural networks based on memristive crossbar circuits

Kataeva

Merrikh-Bayat

Zamanidoost

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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We have adapted backpropagation algorithm for training multilayer perceptron classifier implemented with mem ristive crossbar circuits. The proposed training approach takes into account switching dynamics of a particular, though very typical, type of memristive devices and weight update restrictions imposed by crossbar topology. The simulation results show that for crossbar-based multilayer perceptron with one hidden layer of 300 neurons misclassification rate on MNIST benchmark could be as low as 1.47% and 4.06% for batch and stochastic algorithms, respectively, which is comparable to the best reported results for similar neural networks.

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“…Deep learning was a killer application that made many-core graphics-core (GPU) based processing mainstream, ending the 50-year dominance of single (or few) core CPU systems. Clearly, deep-learning would be a strong motivation for special purpose hardware and [7] and oscillator based solution would be welcome -we are not aware of efforts to this end.…”

Section: Biologically Inspired Network Models and Deep Learning Nets mentioning

confidence: 99%

Perspectives of using spin waves for computing and signal processing

2017

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It is an intriguing concept to use oscillators as fundamental building blocks of electronic computers. The idea is not new, but is currently subject to intense research as a part of the quest for 'beyond Moore' electronic devices. In this paper we give an engineering-minded survey of oscillator-based computing architectures, with the goal of understanding their promise and limitations for next-generation computing. We will mostly discuss non-Boolean, neurally-inspired computing concepts and put the emphasis on hardware and on circuits where the oscillators are realized from emerging, nanoscale building blocks. Despite all the promise that oscillatory computing holds, existing literature gives very few clear-cut arguments about the possible benefits of using oscillators in place of other analog nonlinear circuit elements. In this survey we will argue for finding the rationale of using oscillatory building blocks and call for benchmarking studies that compare oscillatory computing circuits to level-based (analog) implementations.

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A 1 TOPS/W Analog Deep Machine-Learning Engine With Floating-Gate Storage in 0.13 µm CMOS

Cited by 117 publications

References 22 publications

Deep learning for time series classification: a review

Deep learning for time series classification: a review

Efficient training algorithms for neural networks based on memristive crossbar circuits

Perspectives of using spin waves for computing and signal processing

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