US 12,118,460 B2Granted

Discovery of hardware characteristics of deep learning accelerators for optimization via compiler

AssigneeMicron Technology, Inc.

InventorsAliasger Tayeb Zaidy, Marko Vitez, Eugenio Culurciello, Jaime Cummins, Andre Xian Ming Chang

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Abstract

Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with random access memory. A computing device running a compiler can interact and/or probe an integrated circuit device to identify hardware characteristics of the integrated circuit device in performing matrix computations. The compiler can generate and optimize a result of compilation from a description of an artificial neural network based at least in part on the hardware characteristics of the integrated circuit device. The result of compilation can include first data representative of parameters of the artificial neural network and second data representative of instructions executable by the integrated circuit device to generate an output of the artificial neural network based on the first data and an input to the artificial neural network.

Claims20

Dependent claims

A method, comprising:

transmitting, by a computing device, one or more commands to an integrated circuit device to cause the integrated circuit device to generate a response by exercising matrix computations identified by each respective command in the one or more commands, wherein the response for the respective command is indicative of hardware characteristics of the integrated circuit device in performing the matrix computations during the exercising;
determining, by the computing device, hardware characteristics of the integrated circuit device from the response for the respective command;
generating, by the computing device, a result of compilation from data representative of a description of an artificial neural network based at least in part on the hardware characteristics of the integrated circuit device determined from the response to the one or more commands, the result of compilation including first data representative of parameters of the artificial neural network and second data representative of first instructions executable by the integrated circuit device to generate an output of the artificial neural network based on the first data and an input to the artificial neural network;
and
mapping, based on a specification indicating a capability of at least one different type of integrated circuit device, the result of the compilation generated using the description of the artificial neural network into at least one different compilation result for the at least one different type of integrated circuit device to generate second instructions executable on the at least one different type of integrated circuit device.

2 more elements

A computing device, comprising:

memory;
and
at least one microprocessor coupled to the memory and configured to:
probe an integrated circuit device to identify hardware characteristics of the integrated circuit device in performing matrix computations,
generate a result of compilation from data representative of a description of an artificial neural network based at least in part on the hardware characteristics of the integrated circuit device, the result of compilation including first data representative of parameters of the artificial neural network and second data representative of first instructions executable by the integrated circuit device to generate an output of the artificial neural network based on the first data and an input to the artificial neural network, and
mapping, based on a specification indicating a capability of at least one different type of integrated circuit device, the result of the compilation generated using the description of the artificial neural network into at least one different compilation result for the at least one different type of integrated circuit to generate second instructions executable on the at least one different type of integrated circuit.

3 more elements

A non-transitory computer storage medium storing instructions which when executed by a computing device cause the computing device to perform a method, the method comprising:

transmitting, by the computing device, one or more requests to an integrated circuit device to provide one or more responses indicative of hardware characteristics of the integrated circuit device in performing matrix computations;
generating, by the computing device, a result of compilation from data representative of a description of an artificial neural network based at least in part on the hardware characteristics of the integrated circuit device, the result of compilation including first data representative of parameters of the artificial neural network and second data representative of first instructions executable by the integrated circuit device to generate an output of the artificial neural network based on the first data and an input to the artificial neural network;
and
mapping, based on a specification indicating a capability of at least one different type of integrated circuit device, the result of the compilation generated using the description of the artificial neural network into at least one different compilation result for the at least one different type of integrated circuit device to generate second instructions executable on the at least one different type of integrated circuit device.

1 more element

Dependencies are inferred from the claim text.