An Energy-Efficient Deep Convolutional Neural Network Inference Processor With Enhanced Output Stationary Dataflow in 65-nm CMOS

2020 ◽  
Vol 28 (1) ◽  
pp. 87-100 ◽  
Author(s):  
Jaehyeong Sim ◽  
Somin Lee ◽  
Lee-Sup Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Efficient ◽  
Network Inference ◽  
Deep Convolutional Neural Network

An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In Situ Personalization on Smart Devices

2020 ◽  
Vol 55 (10) ◽  
pp. 2691-2702 ◽  
Author(s):  
Seungkyu Choi ◽  
Jaehyeong Sim ◽  
Myeonggu Kang ◽  
Yeongjae Choi ◽  
Hyeonuk Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Efficient ◽  
Deep Convolutional Neural Network ◽  
Smart Devices ◽  
Neural Network Training ◽  
Network Training

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Deep Convolutional Neural Network applied to Chagas Disease Parasitemia Assessment

2020 ◽  
Author(s):  
André Pereira ◽  
Alexandre Pyrrho ◽  
Daniel Vanzan ◽  
Leonardo Mazza ◽  
José Gabriel Gomes
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Chagas Disease ◽  
Deep Convolutional Neural Network
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