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公开(公告)号：US11087170B2
公开(公告)日：2021-08-10
申请号：US16208384
申请日：2018-12-03
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya
Abstract: A generator for generating artificial data, and training for the same. Data corresponding to a first label is altered within a reference labeled data set. A discriminator is trained based on the reference labeled data set to create a selectively poisoned discriminator. A generator is trained based on the selectively poisoned discriminator to create a selectively poisoned generator. The selectively poisoned generator is tested for the first label and tested for the second label to determine whether the generator is sufficiently poisoned for the first label and sufficiently accurate for the second label. If it is not, the generator is retrained based on the data set including the further altered data. The generator includes a first ANN to input first information and output a set of artificial data that is classifiable using a first label and not classifiable using a second label of the set of labeled data.
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公开(公告)号：US20190391850A1
公开(公告)日：2019-12-26
申请号：US16019374
申请日：2018-06-26
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya , Yasuko Eckert
Abstract: Methods and systems for opportunistic load balancing in deep neural networks (DNNs) using metadata. Representative computational costs are captured, obtained or determined for a given architectural, functional or computational aspect of a DNN system. The representative computational costs are implemented as metadata for the given architectural, functional or computational aspect of the DNN system. In an implementation, the computed computational cost is implemented as the metadata. A scheduler detects whether there are neurons in subsequent layers that are ready to execute. The scheduler uses the metadata and neuron availability to schedule and load balance across compute resources and available resources.
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公开(公告)号：US11893502B2
公开(公告)日：2024-02-06
申请号：US15849633
申请日：2017-12-20
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya , Nuwan Jayasena
Abstract: A system assigns experts of a mixture-of-experts artificial intelligence model to processing devices in an automated manner. The system includes an orchestrator component that maintains priority data that stores, for each of a set of experts, and for each of a set of execution parameters, ranking information that ranks different processing devices for the particular execution parameter. In one example, for the execution parameter of execution speed, and for a first expert, the priority data indicates that a central processing unit (“CPU”) executes the first expert faster than a graphics processing unit (“GPU”). In this example, for the execution parameter of power consumption, and for the first expert, the priority data indicates that a GPU uses less power than a CPU. The priority data stores such information for one or more processing devices, one or more experts, and one or more execution characteristics.
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公开(公告)号：US11880715B2
公开(公告)日：2024-01-23
申请号：US17222543
申请日：2021-04-05
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya , Yasuko Eckert
CPC classification number: G06F9/5044 , G06F9/505 , G06F9/5066 , G06N3/082
Abstract: Methods and systems for load balancing in a neural network system using metadata are disclosed. Any one or a combination of one or more kernels, one or more neurons, and one or more layers of the neural network system are tagged with metadata. A scheduler detects whether there are neurons that are available to execute. The scheduler uses the metadata to schedule and load balance computations across compute resources and available resources.
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Patent Agency Ranking
公开(公告)号：US20220147668A1
公开(公告)日：2022-05-12
申请号：US17094690
申请日：2020-11-10
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya , Jakub Kurzak
Abstract: Techniques are disclosed for compressing data. The techniques include identifying, in data to be compressed, a first set of values, wherein the first set of values include a first number of two or more consecutive identical non-zero values; including, in compressed data, a first control value indicating the first number of non-zero values and a first data item corresponding to the consecutive identical non-zero values; identifying, in the data to be compressed, a second value having an exponent value included in a defined set of exponent values; including, in the compressed data, a second control value indicating the exponent value and a second data item corresponding to a portion of the second value other than the exponent value; and including, in the compressed data, a third control value indicating a third set of one or more consecutive zero values in the data to be compressed.
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公开(公告)号：US20210383528A1
公开(公告)日：2021-12-09
申请号：US17030254
申请日：2020-09-23
Applicant: Advanced Micro Devices, Inc. , ATI Technologies ULC
Inventor： Nicholas Malaya , Max Kiehn , Stanislav Ivashkevich
Abstract: A technique for detecting a glitch in an image is provided. The technique includes providing an image to a plurality of individual classifiers to generate a plurality of individual classifier outputs and providing the plurality of individual classifier outputs to an ensemble classifier to generate a glitch classification.
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公开(公告)号：US20210158222A1
公开(公告)日：2021-05-27
申请号：US16694926
申请日：2019-11-25
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya
Abstract: Methods, devices, and systems for emulating a compute kernel with an ANN. The compute kernel is executed on a processor, and it is determined whether the compute kernel is a hotspot kernel. If the compute kernel is a hotspot kernel, the compute kernel is emulated with an ANN, and the ANN is substituted for the compute kernel.
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公开(公告)号：US10970120B2
公开(公告)日：2021-04-06
申请号：US16019374
申请日：2018-06-26
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya , Yasuko Eckert
Abstract: Methods and systems for opportunistic load balancing in deep neural networks (DNNs) using metadata. Representative computational costs are captured, obtained or determined for a given architectural, functional or computational aspect of a DNN system. The representative computational costs are implemented as metadata for the given architectural, functional or computational aspect of the DNN system. In an implementation, the computed computational cost is implemented as the metadata. A scheduler detects whether there are neurons in subsequent layers that are ready to execute. The scheduler uses the metadata and neuron availability to schedule and load balance across compute resources and available resources.
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公开(公告)号：US20200175329A1
公开(公告)日：2020-06-04
申请号：US16208384
申请日：2018-12-03
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya
Abstract: A generator for generating artificial data, and training for the same. Data corresponding to a first label is altered within a reference labeled data set. A discriminator is trained based on the reference labeled data set to create a selectively poisoned discriminator. A generator is trained based on the selectively poisoned discriminator to create a selectively poisoned generator. The selectively poisoned generator is tested for the first label and tested for the second label to determine whether the generator is sufficiently poisoned for the first label and sufficiently accurate for the second label. If it is not, the generator is retrained based on the data set including the further altered data. The generator includes a first ANN to input first information and output a set of artificial data that is classifiable using a first label and not classifiable using a second label of the set of labeled data.
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