Marvell Asia Pte., Ltd.

Cavium, Inc.

Magiq Technologies, Inc.

Harri Hakkarainen

Ulf Hanebutte

Chien-Chun Chou

Senad Durakovic

Muhammad Raghib Hussain

Veena Karthikeyan

Avinash Sodani

Fu-Hwa Wang

Amer Haider

Audrius Berzanskis

Trent Parker

Keun Lee

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 new approach is proposed to support correlating high-level code with low-level instructions of an application running on a hardware. A compiler that compiles a high-level function in the high-level code of the application into a set of low-level instructions to be executed on the hardware is configured to utilize one or more reserved fields of the set of low-level instructions to incorporate one or more IDs and an actionable item. The IDs are mapped to the high-level function, wherein such mapping is programmable by the compiler. Based on the mapped IDs and the actionable item incorporated in the set of the low-level instructions, the runtime performance of the application on the hardware can be monitored and profiled and issues related to the high-level code of the application can be identified for debugging purposes.

Method and apparatus for correlating high-level code with low-level instructions for machine learning applications

A method includes receiving a first set of data. The method also includes receiving an instruction to determine a largest value within the first set of data. The first set of data is divided into a first plurality of data portions based on a hardware architecture of a first plurality of processing elements. The first plurality of data portions is mapped to the first plurality of processing elements. Each data portion of the first plurality of data portions is mapped exclusively to a processing element of the first plurality of processing elements. Each data portion of the first plurality of data portions is processed by its respective processing element to identify a largest value from each data portion of the first plurality of data portions, wherein the processing forms a first output data comprising the largest value from the each data portion of the first plurality of data portions.

Method and apparatus for performing machine learning operations in parallel on machine learning hardware

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

A method includes receiving a high-level function in a high-level code of an application; identifying resources in a hardware to execute a set of low-level instructions that is generated from the high-level function in the high-level code; compiling the high-level function in the high-level code of the application into the set of low-level instructions to be executed on the hardware; and generating a plurality of structured metadata associated with allocation of resources in the hardware to execute the set of low-level instructions.

Method and apparatus for generating metadata by a compiler

A method includes receiving a high-level function in a high-level code of an application is received. The method also include identifying resources in a hardware to execute a set of low-level instructions that is generated from the high-level function in the high-level code. One or more processing operations are determined to be performed that is associated with the high-level function in the high-level code. The determining of the one or more processing operations occurs based on architecture of the hardware. The high-level function in the high-level code of the application is compiled into the set of low-level instructions to be executed on the hardware. A plurality of structured metadata is generated and includes information associated with the determining resources in the hardware and further includes information associated with the determining one or more processing operations.

Disclosed are systems and methods for protecting secret device keys, such as High-bandwidth Digital Content Protection (HDCP) device keys. Instead of storing secret device keys in the plain, a security algorithm and one or more protection keys are stored on the device. The security algorithm is applied to the secret device keys and the one or more protection keys to produce encrypted secret device keys. The encrypted secret device keys are then stored either on chip or off-chip.

System and method for secure device key storage

A method of integrating quantum key distribution (QKD) with Internet protocol security (IPSec) to improve the security of IPSec. Standard IPSec protocols impose limits on the frequency at which keys can be changed. This makes efforts to improve the security of IPSec by employing quantum keys problematic. The method includes employing multiple security associations (SA) in in-bound and outbound SA Tables in a manner that enables a high key flipping rate and that enables combining quantum keys with classical keys generated by Internet Key Exchange (IKE), thereby enabling QKD-based IPSec.

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Los Gatos, CA, United States of America

Harri Hakkarainen