Compression of machine learning models utilizing pseudo-labeled data training

Compression of machine learning models utilizing pseudo-labeled data training

Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coup...

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Hauptverfasser: Poornachandran, Rajesh, Zhuang, Guozhong, Ould-ahmed-vall, Elmoustapha, Justin, Jerin C, Ramani, Pradeep, Surti, Prasoonkumar, Thomas, Anil, Keskin, Gokce, Elibol, Oguz H, Harihara, Rama, Ray, Joydeep, Ashbaugh, Ben, Huang, Jing, Subramanian, Bhavani, Cui, Xiaoming, Balasubramanian, Kumar, Costa, Timothy B, Gong, Ting, Bobba, Jayaram, Sakthivel, Chandrasekaran
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
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Zusammenfassung:Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.