Marvell Asia Pte., Ltd.

Veena Karthikeyan

Ulf Hanebutte

Senad Durakovic

Chien-Chun Chou

Harri Hakkarainen

Fu-Hwa Wang

Yao Chou

A method includes receiving a high-level function in a first high-level code; compiling the high-level function into a first set of low-level instructions to be executed on a hardware or a simulator; transmitting the first set of low-level instructions to the hardware or the simulator; receiving a plurality of statistical data generated by the hardware or the simulator in response to execution of the first set of low-level instructions, wherein the plurality of statistical data is performance related; determining whether to make changes to the compilation associated with the high-level function in the first high-level code based on the plurality of statistical data; recompiling the high-level function into a second set of low-level instructions to be executed on the hardware or the simulator based on the changes to the compilation; and transmitting the second set of low-level instructions to the hardware or the simulator.

Method and system for code optimization based on statistical data

A system to support validation and debugging of compiled low-level instructions for a machine learning (ML) network model on an ML-specific hardware. A compiler identifies well-defined boundaries in the ML network model based on primitives used to generate low-level instructions for the hardware. The ML network model is partitioned into units/layers/sub-graphs based on the plurality of well-defined boundaries. The compiler then generates an internal representation for each of the units wherein the internal representation is mapped to components in the hardware. Each of the units is compiled into a first set to be executed on the ML-specific hardware and a second set to be executed on a second computing device. The output results from executing the two sets of low-level instructions are compared to validate the first set of low-level instructions. If the outputs do not match fully, the first set of low-level instructions is debugged and recompiled.

Method and apparatus for compiler and low-level instruction validation of machine learning operations on hardware

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Mountain View, CA, United States of America

Veena Karthikeyan