Figure 2: Some Applications of Artificial Intelligence



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2
To develop an accelerator that can deliver state-of-the-art 
accuracy with minimum energy consumption when 
using large CNNs(Chen et al., 2017).
Implementation of an Energy-Efficient 
Reconfigurable Accelerator for Deep 
CNN using efficient dataflow to 
minimize energy through zeros 
skipping/gating.
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5. Deep Learning Variants
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Related Works
S/N
Research Focus
Contribution
1
To demonstrate the advantage of combining deep neural 
networks with support vector machines (Zhong et al., 
2000; Nagi et al., 2012; Tang et al., 2013; Li et al., 2017).
Introduction of a novel classifier 
architecture that combines two 
heterogeneous supervised
classification techniques, CNN and 
SVM for feature extraction and for 
classification
2
To exploit the power of deep neural networks in 
optimizing the performance of nearest neighbor 
classifiers in kNN Classification tasks (Min et al., 2009; 
Ren et al., 2014).
They presented a framework for 
learning convolutional nonlinear 
features for K nearest neighbor (kNN) 
classification.
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Application 
Literature 
Review
Deep learning has been applied in so many ways to solve real life problems among which are:
Methodology
Application of DL
1
Domain
Application of DL
2
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(1)
Methodology Application of DL

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