HAL: Computer System for Scalable Deep Learning

Author(s):  
Volodymyr Kindratenko ◽  
Dawei Mu ◽  
Yan Zhan ◽  
John Maloney ◽  
Sayed Hadi Hashemi ◽  
...  
2021 ◽  
Vol 3 (1) ◽  
pp. 66-74
Author(s):  
Ranganathan G

In the near future, deep learning algorithms will be incorporated in several applications for assisting the human beings. The deep learning algorithms have the tendency to allow a computer to work on its assumption. Most of the deep learning algorithms mimic the human brain’s neuron connection to leverage an artificial intelligence to the computer system. This helps to improve the operational speed and accuracy on several critical tasks. This paper projects the blocks, which are required for the incorporation of deep learning based algorithm. Also, the paper attempts to deeply analyze the necessity of the preprocessing step over several deep learning based applications.


1979 ◽  
Vol 44 ◽  
pp. 41-47
Author(s):  
Donald A. Landman

This paper describes some recent results of our quiescent prominence spectrometry program at the Mees Solar Observatory on Haleakala. The observations were made with the 25 cm coronagraph/coudé spectrograph system using a silicon vidicon detector. This detector consists of 500 contiguous channels covering approximately 6 or 80 Å, depending on the grating used. The instrument is interfaced to the Observatory’s PDP 11/45 computer system, and has the important advantages of wide spectral response, linearity and signal-averaging with real-time display. Its principal drawback is the relatively small target size. For the present work, the aperture was about 3″ × 5″. Absolute intensity calibrations were made by measuring quiet regions near sun center.


Author(s):  
Stellan Ohlsson
Keyword(s):  

JAMA ◽  
1966 ◽  
Vol 196 (11) ◽  
pp. 967-972
Author(s):  
J. F. Dickson

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