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公开(公告)号：US20200272892A1
公开(公告)日：2020-08-27
申请号：US16797871
申请日：2020-02-21
IPC分类号： G06N3/063 , G06N3/08 , G06F12/0804 , G06T1/60
摘要： Techniques including receiving a first set of values for processing by a machine learning (ML) network, storing a first portion of the first set of values in an on-chip memory, processing the first portion of the first set of values in a first layer of the ML network to generate a second portion of a second set of values, overwriting the stored first portion with the generated second portion, processing the second portion in a second layer of the ML network to generate a third portion of a third set of values, storing the third portion, repeating the steps of storing the first portion, processing the first portion, overwriting the stored first portion, processing the second portion, and storing the third portion for a fourth portion of the first set of values until all portions of the first set of values are processed to generate the third set of values.
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公开(公告)号：US20230064481A1
公开(公告)日：2023-03-02
申请号：US17463341
申请日：2021-08-31
摘要： An electronic device, comprising one or more processors, wherein the one or more processors are configured to execute instructions causing the one or more processors to: receive a machine learning (ML) model and execution information associated with the ML model, wherein the execution information including first execution data indicating how to execute the ML model optimized based on a first performance criterion, and second execution data execution data indicating how to execute the ML model optimized based on a second performance criteria, the second performance criterion different from the first performance criteria; execute the ML model based on the first execution data; determine to execute the ML model based on the second execution data; and execute the ML model based on the second execution data.
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公开(公告)号：US20220012635A1
公开(公告)日：2022-01-13
申请号：US17327869
申请日：2021-05-24
发明人： Rishabh GARG , Pramod Kumar SWAMI , Kumar DESAPPAN , Anshu JAIN
摘要： Techniques for enhancing machine learning (ML) model execution. The technique includes determining an amount of memory used to process layers of a machine learning network having multiple layers, smoothing the amount of memory used to process the layers of the machine learning network based on a number of layers, identifying change layers where the smoothed amount of memory used changes more than a memory change threshold amount, grouping the layers of the machine learning network into a first layer grouping based on the identified change layers, and outputting the first layer grouping.
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