Invention Grant
- Patent Title: Machine learning training architecture for programmable devices
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Application No.: US16585857Application Date: 2019-09-27
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Publication No.: US11210063B2Publication Date: 2021-12-28
- Inventor: Martin Langhammer , Bogdan Pasca , Sergey Gribok , Gregg William Baeckler , Andrei Hagiescu
- 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, PC
- Main IPC: G06F7/487
- IPC: G06F7/487 ; G06F7/501 ; H03M7/24 ; G06F9/30 ; G06F17/16

Abstract:
A programmable device may be configured to support machine learning training operations using matrix multiplication circuitry implemented on a systolic array. The systolic array includes an array of processing elements, each of which includes hybrid floating-point dot-product circuitry. The hybrid dot-product circuitry has a hard data path that uses digital signal processing (DSP) blocks operating in floating-point mode and a hard/soft data path that uses DSP blocks operating in fixed-point mode operated in conjunction with general purpose soft logic. The hard/soft data path includes 2-element dot-product circuits that feed an adder tree. Results from the hard data path are combined with the adder tree using format conversion and normalization circuitry. Inputs to the hybrid dot-product circuitry may be in the BFLOAT16 format. The hard data path may be in the single precision format. The hard/soft data path uses a custom format that is similar to but different than BFLOAT16.
Public/Granted literature
- US20200026494A1 MACHINE LEARNING TRAINING ARCHITECTURE FOR PROGRAMMABLE DEVICES Public/Granted day:2020-01-23
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