Invention Grant
- Patent Title: Selecting computational kernel variants using neural networks
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Application No.: US16723608Application Date: 2019-12-20
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Publication No.: US11625605B2Publication Date: 2023-04-11
- Inventor: Jonathan Edward Barker , Christopher Thomas Cheng , Paul Martin Springer , Wojciech Jablonski
- Applicant: Nvidia Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Nvidia Corporation
- Current Assignee: Nvidia Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Hogan Lovells US LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F17/16 ; G06F7/57 ; G06N3/04

Abstract:
Apparatuses, systems, and techniques to optimize kernel selection for performing a computation. In at least one embodiment, a neural network is trained and utilized to generate a list of kernels so that an (e.g., optimal) kernel may be identified. The neural network receives characteristics of the input matrices and determines relevancy scores for a list of possible kernels. Based on an ordered listing of kernels by relevant score, a kernel is selected from the list and utilized to perform the computation and provide the result.
Public/Granted literature
- US20210192334A1 SELECTING COMPUTATIONAL KERNEL VARIANTS USING NEURAL NETWORKS Public/Granted day:2021-06-24
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