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公开(公告)号：US20220027674A1
公开(公告)日：2022-01-27
申请号：US17397249
申请日：2021-08-09
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：US20210383527A1
公开(公告)日：2021-12-09
申请号：US17030250
申请日：2020-09-23
Applicant: Advanced Micro Devices, Inc. , ATI Technologies ULC
Inventor： Nicholas Malaya , Max Kiehn
Abstract: A technique for generating a trained discriminator is provided. The technique includes applying one or more of a glitched image or an unglitched image to a discriminator; receiving classification output from the discriminator; adjusting weights of the discriminator to improve classification accuracy of the discriminator; applying noise to a generator; receiving an output image from the generator; applying the output image to the discriminator to obtain a classification; and adjusting weights of one of the discriminator or the generator to improve ability of the generator to reduce classification accuracy of the discriminator, based on the classification.
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公开(公告)号：US20230409982A1
公开(公告)日：2023-12-21
申请号：US18456057
申请日：2023-08-25
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：US11557026B2
公开(公告)日：2023-01-17
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：US20190005377A1
公开(公告)日：2019-01-03
申请号：US15638993
申请日：2017-06-30
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya
Abstract: Training devices and methods for training an artificial neural network (ANN). The training device includes processing circuitry configured to transmit training data for the ANN and parameters for the ANN to an inference device. The processing circuitry is also configured to receive inference data, based on the training data and the parameters, from the inference device. The processing circuitry is also configured to receive inference timing information, based on the training data and the parameters, from the inference device. The processing circuitry is also configured to calculate a difference between the calculated inference data and expected inference data. The processing circuitry is also configured to modify the parameters and to transmit the modified parameters to the inference device if the difference exceeds a difference threshold or if the timing information indicates an inference time exceeding a timing threshold
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Patent Agency Ranking
公开(公告)号：US11640711B2
公开(公告)日：2023-05-02
申请号：US17030250
申请日：2020-09-23
Applicant: Advanced Micro Devices, Inc. , ATI Technologies ULC
Inventor： Nicholas Malaya , Max Kiehn
Abstract: A technique for generating a trained discriminator is provided. The technique includes applying one or more of a glitched image or an unglitched image to a discriminator; receiving classification output from the discriminator; adjusting weights of the discriminator to improve classification accuracy of the discriminator; applying noise to a generator; receiving an output image from the generator; applying the output image to the discriminator to obtain a classification; and adjusting weights of one of the discriminator or the generator to improve ability of the generator to reduce classification accuracy of the discriminator, based on the classification.
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公开(公告)号：US20210224130A1
公开(公告)日：2021-07-22
申请号：US17222543
申请日：2021-04-05
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya , Yasuko Eckert
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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公开(公告)号：US20190188577A1
公开(公告)日：2019-06-20
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：US12112270B2
公开(公告)日：2024-10-08
申请号：US17397249
申请日：2021-08-09
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya
IPC: G06N3/084 , G06F18/21 , G06F18/214 , G06F18/28 , G06N3/04 , G06N3/088 , G06V10/764 , G06V10/774 , G06V10/82
CPC classification number: G06N3/084 , G06F18/2148 , G06F18/217 , G06F18/28 , G06N3/04 , G06N3/088 , G06V10/764 , G06V10/774 , G06V10/82
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：US11741397B2
公开(公告)日：2023-08-29
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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