scholarly journals A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

2016 ◽  
Author(s):  
Thomas E. Potok ◽  
Catherine D. Schuman ◽  
Steven R. Young ◽  
Robert M. Patton ◽  
Federico Spedalieri ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Quantum Computers ◽  
Learning Networks

scholarly journals A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Thomas E. Potok ◽  
Catherine Schuman ◽  
Steven Young ◽  
Robert Patton ◽  
Federico Spedalieri ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Quantum Computers ◽  
Learning Networks

scholarly journals Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7018
Author(s):  
Justin Lo ◽  
Jillian Cardinell ◽  
Alejo Costanzo ◽  
Dafna Sussman
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Data Augmentation ◽  
Search Algorithm ◽  
High Dimensional ◽  
Learning Networks ◽  
Imaging Data ◽  
Covariance Matrix Adaptation ◽  
Using Data ◽  
Adaptation Evolution

Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited. Their limitation stems from them requiring ample data to achieve high performance and adequate generalizability. In the case of clinical imaging data, images are not always available in large quantities. This issue can be alleviated by using data augmentation (DA) techniques. The choice of DA is important because poor selection can possibly hinder the performance of a DL algorithm. We propose a DA policy search algorithm that offers an extended set of transformations that accommodate the variations in biomedical imaging datasets. The algorithm makes use of the efficient and high-dimensional optimizer Bi-Population Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) and returns an optimal DA policy based on any input imaging dataset and a DL algorithm. Our proposed algorithm, Medical Augmentation (Med-Aug), can be implemented by other researchers in related medical DL applications to improve their model’s performance. Furthermore, we present our found optimal DA policies for a variety of medical datasets and popular segmentation networks for other researchers to use in related tasks.

scholarly journals BPLight-CNN: A Photonics-Based Backpropagation Accelerator for Deep Learning

2021 ◽  
Vol 17 (4) ◽  
pp. 1-26
Author(s):  
Dharanidhar Dang ◽  
Sai Vineel Reddy Chittamuru ◽  
Sudeep Pasricha ◽  
Rabi Mahapatra ◽  
Debashis Sahoo
Keyword(s):  
Deep Learning ◽  
Computational Efficiency ◽  
High Performance ◽  
Energy Savings ◽  
State Of The Art ◽  
The State ◽  
Bp Algorithm ◽  
Learning Networks ◽  
Training Module ◽  
Lower Accuracy

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation (BP) algorithm. This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pretrained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high-performance deep learning training. We present the design for a convolutional neural network (CNN), BPLight-CNN , which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models, including LeNet and VGG-Net. The proposed design achieves (i) at least 34× speedup, 34× improvement in computational efficiency, and 38.5× energy savings during training; and (ii) 29× speedup, 31× improvement in computational efficiency, and 38.7× improvement in energy savings during inference compared with the state-of-the-art designs. All of these comparisons are done at a 16-bit resolution, and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared with the state-of-the-art.

scholarly journals Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 494
Author(s):  
Seid Koric ◽  
Diab W. Abueidda
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Continuous Caster ◽  
Learning Networks ◽  
Learning Sequence ◽  
Temperature Dependent ◽  
Multiphysics Modeling ◽  
Highly Nonlinear ◽  
Testing Data ◽  
Modeling Data

The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

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