- Patent Title: Compute optimizations for low precision machine learning operations
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Application No.: US16197821Application Date: 2018-11-21
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Publication No.: US10853906B2Publication Date: 2020-12-01
- Inventor: Elmoustapha Ould-Ahmed-Vall , Sara S. Baghsorkhi , Anbang Yao , Kevin Nealis , Xiaoming Chen , Altug Koker , Abhishek R. Appu , John C. Weast , Mike B. Macpherson , Dukhwan Kim , Linda L. Hurd , Ben J. Ashbaugh , Barath Lakshmanan , Liwei Ma , Joydeep Ray , Ping T. Tang , Michael S. Strickland
- 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: G06T1/20
- IPC: G06T1/20 ; G06F7/483 ; G06N3/08 ; G06F9/30 ; G06N3/04 ; G06N3/063 ; G06F9/50 ; G06F9/38 ; G06N20/00 ; G06F3/14 ; G06T1/60 ; G06T15/00

Abstract:
One embodiment provides an accelerator module comprising a memory stack including multiple memory dies; a graphics processing unit (GPU) coupled with the memory stack via one or more memory controllers, the GPU including a plurality of multiprocessors having a single instruction, multiple thread (SIMT) architecture, the multiprocessors to execute at least one single instruction. The at least one single instruction is to cause at least a portion of the GPU to perform a floating point operation on input having differing precisions. The floating point operation is a two-dimensional matrix multiply and accumulate operation.
Public/Granted literature
- US20190206020A1 COMPUTE OPTIMIZATIONS FOR LOW PRECISION MACHINE LEARNING OPERATIONS Public/Granted day:2019-07-04
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