Run-time neural network re-allocation across heterogeneous processors

    公开(公告)号:US11494238B2

    公开(公告)日:2022-11-08

    申请号:US16506913

    申请日:2019-07-09

    Abstract: Neural network workload re-allocation in a system-on-chip having multiple heterogenous processors executing one or more neural network units may be based on measurements associated with the processors' conditions and on metadata associated with the neural network units. Metadata may be contained in an input file along with neural network information. Measurements characterizing operation of the processors may be obtained and compared with one or more thresholds. A neural network unit executing on a processor may be identified as a candidate for re-allocation based on metadata associated with the neural network unit and results of the comparisons. A target processor may be identified based on the metadata and results of the comparisons, and the candidate neural network neural network unit may be re-allocated to the target processor.

    Application profile driven scheduling and configuration in a system on a chip

    公开(公告)号:US09940109B2

    公开(公告)日:2018-04-10

    申请号:US14803110

    申请日:2015-07-20

    Abstract: Various embodiments of methods and systems for proactive resource allocation and configuration are disclosed. An exemplary method first compiles and links a profile instrumented application with a compiler comprising a profile guided optimization feature that inserts calls to a profiler runtime. The profile instrumented application is executed on a target device using one or more workload datasets representative of probable workloads. During execution, based on recognition of the inserted calls, an instrumentation-based profile dataset is generated in association with each of the one or more workload datasets. Next, the profile instrumented application is recompiled and relinked based on the instrumentation-based profile datasets to create a set of profile guided optimizations to the source code, thereby resulting in an optimized application. The optimized application may be executed and monitored to generate a revised profile dataset useful for providing instructions to the target device for optimal workload allocation and resource configuration.

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