| 2014-CULU-66770 | |
| Tagging images and videos on mobile devices is an open market. Many software companies recently began to work on deep learning techniques; however, such techniques are limited by the low-performance hardware of mobile phones. In addition, some of the existing products are task-specific and cannot be reprogramed to perform new tasks. Researchers at Purdue University have developed a new system of hardware and software that can solve state-of-the-art, complex, vision tasks. This system includes a novel hardware architecture that provides low-power operation and simultaneously a high number of operations for a whole class of vision algorithms while maintaining complete programmability. This system adapts new tasks with minor reprogramming and scale more efficiently to new vision models. Advantages: -Can solve state-of-the-art, complex, vision tasks -Provides low-power operation and simultaneously a high number of operations -Adapt new tasks with minor reprogramming -Scale more efficiently to new vision models |
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Dec 31, 2017
CON-Patent
United States
10,157,156
Dec 18, 2018
Mar 17, 2015
Utility Patent
United States
9,858,220
Jan 2, 2018
Mar 17, 2014
Provisional-Patent
United States
(None)
(None)
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