- Patent Title: Hybrid floating point representation for deep learning acceleration
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Application No.: US17128407Application Date: 2020-12-21
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Publication No.: US11620105B2Publication Date: 2023-04-04
- Inventor: Naigang Wang , Jungwook Choi , Kailash Gopalakrishnan , Ankur Agrawal , Silvia Melitta Mueller
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Garg Law Firm, PLLC
- Agent Rakesh Garg; Jared Chaney
- Main IPC: G06F7/483
- IPC: G06F7/483 ; G06N3/08 ; G06N3/084

Abstract:
In an embodiment, a method includes configuring a specialized circuit for floating point computations using numbers represented by a hybrid format, wherein the hybrid format includes a first format and a second format. In the embodiment, the method includes operating the further configured specialized circuit to store an approximation of a numeric value in the first format during a forward pass for training a deep learning network. In the embodiment, the method includes operating the further configured specialized circuit to store an approximation of a second numeric value in the second format during a backward pass for training the deep learning network.
Public/Granted literature
- US20210109709A1 HYBRID FLOATING POINT REPRESENTATION FOR DEEP LEARNING ACCELERATION Public/Granted day:2021-04-15
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