Invention Grant
- Patent Title: Deep learning model scheduling
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Application No.: US16018784Application Date: 2018-06-26
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Publication No.: US11526728B2Publication Date: 2022-12-13
- Inventor: Minjia Zhang , Samyam Rajbhandari , Wenhan Wang , Yuxiong He
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Schwegman Lundberg & Woessner, P.A.
- Main IPC: G06F17/16
- IPC: G06F17/16 ; G06N3/04 ; G06N3/08 ; G06N3/063

Abstract:
Systems, methods, and computer-executable instructions for determining a computation schedule for a recurrent neural network (RNN). A matrix multiplication (MM) directed-acyclic graph (DAG) is received for the RNN. Valid phased computation schedules for the RNN are generated. Each of the valid phase computation schedule includes an ordering of MM operations. For each of the plurality of valid phased computation schedules, each of the MM operations is partitioned to processor cores based on L3 cache to L2 cache data movement. The RNN is executed based on the valid phased computation schedules. A final computation schedule is stored. The final computation schedule is used for future executions of the RNN.
Public/Granted literature
- US20190311245A1 DEEP LEARNING MODEL SCHEDULING Public/Granted day:2019-10-10
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