Silicon spiking neurons for hardware implementation of extreme learning machines

Basu, Arindam; Sun, Shuo; Zhou, Hongming; Lim, Meng‐Hiot; Huang, Guang-Bin

doi:10.1016/j.neucom.2012.01.042

Cited by 76 publications

(37 citation statements)

References 17 publications

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“…In [84], the components of VLSI implementation of a spiking neural network is presented while [85] demonstrates a highly configurable neuromorphic chip with integrated learning for a network of spiking neurons which can be used in pattern classification, recognition, and associative memory tasks. A spatial architecture named 'Eyeriss' for energy-efficient dataflow for Convolutional Neural Networks is implemented in [86].…”

Section: Advanced Technologies For ML Hardware Architecturementioning

confidence: 99%

Hardware Design for Machine Learning

Jawandhiya¹

2018

IJAIA

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Section: Advanced Technologies For ML Hardware Architecturementioning

confidence: 99%

Hardware Design for Machine Learning

Jawandhiya¹

2018

IJAIA

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“…Where g(a,b,x) is an activation function satisfying ELM universal approximation capability theorems [11]. In fact, any nonlinear piecewise continuous functions (e.g.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Recent Trends in ELM and MLELM: A review

Parkavi¹,

Shanthi²,

Bhuvaneshwari³

2017

Adv. sci. technol. eng. syst. j.

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“…Numerous implementations of multi-layered extreme learning machines have been developed [27][28][29], and it was found that the multi-layer implementation of extreme learning machine performed better than the conventional ELM in term of the recognition and classification performance. Recent works that were intended to develop neuromorphic implementations of Extreme Learning Machines [30][31][32] motivated this current work. Further details of neuromorphic implementations are described elsewhere [33].…”

Section: Introductionmentioning

confidence: 99%

Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

et al. 2017

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Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

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Silicon spiking neurons for hardware implementation of extreme learning machines

Cited by 76 publications

References 17 publications

Hardware Design for Machine Learning

Hardware Design for Machine Learning

Recent Trends in ELM and MLELM: A review

Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

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