Invention Grant
- Patent Title: Incremental precision networks using residual inference and fine-grain quantization
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Application No.: US18060414Application Date: 2022-11-30
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Publication No.: US11893490B2Publication Date: 2024-02-06
- Inventor: Abhisek Kundu , Naveen Mellempudi , Dheevatsa Mudigere , Dipankar Das
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Mendonsa & Hamilton LLP
- Agent Jaffery Watson
- Priority: IN 1741015052 2017.04.28
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N5/04 ; G06T15/00 ; G06F9/46 ; G06N3/063 ; G06N3/084 ; G06N3/044 ; G06N3/045 ; G06T17/20 ; G06T15/80 ; G06T17/10 ; G06T15/04 ; G06V10/94

Abstract:
One embodiment provides for a computer-readable medium storing instructions that cause one or more processors to perform operations comprising determining a per-layer scale factor to apply to tensor data associated with layers of a neural network model and converting the tensor data to converted tensor data. The tensor data may be converted from a floating point datatype to a second datatype that is an 8-bit datatype. The instructions further cause the one or more processors to generate an output tensor based on the converted tensor data and the per-layer scale factor.
Public/Granted literature
- US20230087364A1 INCREMENTAL PRECISION NETWORKS USING RESIDUAL INFERENCE AND FINE-GRAIN QUANTIZATION Public/Granted day:2023-03-23
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