Invention Grant
- Patent Title: Circuitry for low-precision deep learning
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Application No.: US15994930Application Date: 2018-05-31
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Publication No.: US11275998B2Publication Date: 2022-03-15
- Inventor: Martin Langhammer , Sudarshan Srinivasan , Gregg William Baeckler , Duncan Moss , Sasikanth Avancha , 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: Fletcher Yoder, P.C.
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06N3/063 ; G06F17/16 ; G06F7/501 ; G06F5/01 ; G06F7/509 ; H03M7/40 ; H03M7/42 ; H03M7/30

Abstract:
The present disclosure relates generally to techniques for improving the implementation of certain operations on an integrated circuit. In particular, deep learning techniques, which may use a deep neural network (DNN) topology, may be implemented more efficiently using low-precision weights and activation values by efficiently performing down conversion of data to a lower precision and by preventing data overflow during suitable computations. Further, by more efficiently mapping multipliers to programmable logic on the integrated circuit device, the resources used by the DNN topology to perform, for example, inference tasks may be reduced, resulting in improved integrated circuit operating speeds.
Public/Granted literature
- US20190042939A1 CIRCUITRY FOR LOW-PRECISION DEEP LEARNING Public/Granted day:2019-02-07
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