Invention Grant
- Patent Title: Compression of machine learning models utilizing pseudo-labeled data training
-
Application No.: US18466141Application Date: 2023-09-13
-
Publication No.: US12056906B2Publication Date: 2024-08-06
- Inventor: Joydeep Ray , Ben Ashbaugh , Prasoonkumar Surti , Pradeep Ramani , Rama Harihara , Jerin C. Justin , Jing Huang , Xiaoming Cui , Timothy B. Costa , Ting Gong , Elmoustapha Ould-ahmed-vall , Kumar Balasubramanian , Anil Thomas , Oguz H. Elibol , Jayaram Bobba , Guozhong Zhuang , Bhavani Subramanian , Gokce Keskin , Chandrasekaran Sakthivel , Rajesh Poornachandran
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: INTEL CORPORATION
- Current Assignee: INTEL CORPORATION
- Current Assignee Address: US CA Santa Clara
- Agency: JAFFERY WATSON MENDONSA & HAMILTON LLP
- Main IPC: G06F12/02
- IPC: G06F12/02 ; G06T9/00 ; G06T15/00

Abstract:
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.
Public/Granted literature
- US20240070926A1 COMPRESSION OF MACHINE LEARNING MODELS UTILIZING PSEUDO-LABELED DATA TRAINING Public/Granted day:2024-02-29
Information query