Low Cost and Low Power Stacked Sparse Autoencoder Hardware Acceleration for Deep Learning Edge Computing Applications

2020 ◽  
Author(s):  
Tarek Belabed ◽  
Maria Gracielly F. Coutinho ◽  
Marcelo A. C. Fernandes ◽  
Valderrama Carlos ◽  
Chokri Souani
Keyword(s):  
Deep Learning ◽  
Low Power ◽  
Low Cost ◽  
Hardware Acceleration ◽  
Edge Computing ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

scholarly journals Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

2021 ◽  
Author(s):  
Gracielly G. F. Coutinho ◽  
Gabriel B. M. Câmara ◽  
Raquel de M. Barbosa ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Deep Learning ◽  
Viral Genome ◽  
Genomic Sequence ◽  
Confusion Matrix ◽  
Taxonomic Classification ◽  
Classification Problems ◽  
Virus Identification ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

scholarly journals Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Liu ◽  
Lianfeng Li ◽  
Jian Ma
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Short Time Fourier Transform ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Short Time

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

scholarly journals User Driven FPGA-Based Design Automated Framework of Deep Neural Networks For Low-Power Low-Cost Edge Computing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
TaRek Belabed ◽  
Maria Gracielly F. Coutinho ◽  
Marcelo A. C. Fernandes ◽  
Carlos Valderrama ◽  
Chokri Souani
Keyword(s):  
Neural Networks ◽  
Low Power ◽  
Deep Neural Networks ◽  
Low Cost ◽  
Edge Computing

scholarly journals Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Wenliao Du ◽  
Shuangyuan Wang ◽  
Xiaoyun Gong ◽  
Hongchao Wang ◽  
Xingyan Yao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Translation Invariance ◽  
Complex Wavelet Transform ◽  
Translational Invariance ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Discriminative feature extraction is a challenge for data-driven fault diagnosis. Although deep learning algorithms can automatically learn a good set of features without manual intervention, the lack of domain knowledge greatly limits the performance improvement, especially for nonstationary and nonlinear signals. This paper develops a multiscale information fusion-based stacked sparse autoencoder fault diagnosis method. The autoencoder takes advantage of the multiscale normalized frequency spectrum information obtained by dual-tree complex wavelet transform as input. Accordingly, the multiscale normalized features guarantee the translational invariance for signal characteristics, and the stacked sparse autoencoder benefits the unsupervised feature learning and ensures accurate and stable diagnosis performance. The developed method is performed on motor bearing vibration signals and worm gearbox vibration signals, respectively. The results confirm that the developed method can accommodate changing working conditions, be free of manual feature extraction, and perform better than the existing intelligent diagnosis methods.

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