公开(公告)号：US20240160892A1

公开(公告)日：2024-05-16

申请号：US18414068

申请日：2024-01-16

Applicant: samsung electronics co., ltd.

Inventor： Prateek KESERWANI ,  Srinivas Soumitri MIRIYALA ,  Vikram Nelvoy RAJENDIRAN ,  Pradeep NELAHONNE SHIVAMURTHAPPA

IPC: G06N3/04

CPC classification number: G06N3/04

Abstract: A method for selecting an artificial intelligence (AI) model in neural architecture search, includes: measuring a scale of receptive field for a plurality of neural network layers corresponding to each of a plurality of candidate AI models; determining a first score for a first group of neural network layers among the plurality of neural network layers based on the scale of the receptive field for the first group of neural network layers, the scale of the receptive field for each of the first group of neural network layers being smaller than a size of an object; determining a second score for a second group of neural network layers among the plurality of neural network layers based on the scale of the receptive field for the second group of neural network layers, the scale of the receptive field for each of the second group of neural network layers being greater than the size of the object; determining a third score for each of the plurality of candidate AI models as a function of the first score and the second score; and selecting, based on the third score, a candidate AI model among the plurality of candidate AI models for training and deployment, the candidate AI model having a highest third score among the third scores of the plurality of candidate AI models.

公开(公告)号：US20230127001A1

公开(公告)日：2023-04-27

申请号：US18082305

申请日：2022-12-15

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Mayukh DAS ,  Brijraj SINGH ,  Pradeep NELAHONNE SHIVAMURTHAPPA ,  Aakash KAPOOR ,  Rajath Elias SOANS ,  Soham Vijay DIXIT ,  Sharan Kumar ALLUR ,  Venkappa MALA

IPC: G06N3/082 ,  G06V10/776 ,  G06V10/82 ,  G06V40/16

Abstract: A method for generating an optimal neural network (NN) model may include determining intermediate outputs of the NN model by passing an input dataset through each intermediate exit gate of the plurality of intermediate exit gates, determining an accuracy score for each intermediate exit gate of the plurality of intermediate exit gates based on a comparison of the final output of the NN model with the intermediate output, identifying an earliest intermediate exit gate that produces the intermediate output closer to the final output based on the accuracy score, and generating the optimal NN model by removing remaining layers of the plurality of layers and remaining intermediate exit gates of the plurality of intermediate exit gates located after the determined earliest intermediate exit gate.

公开(公告)号：US20230068381A1

公开(公告)日：2023-03-02

申请号：US17961453

申请日：2022-10-06

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Tejpratap Venkata Subbu Lakshmi GOLLANAPALLI ,  Arun ABRAHAM ,  Raja KUMAR ,  Pradeep NELAHONNE SHIVAMURTHAPPA ,  Vikram Nelvoy RAJENDIRAN ,  Prasen Kumar SHARMA

IPC: G06N3/08 ,  G06V10/82

Abstract: Various embodiments of the disclosure disclose a method for quantizing a Deep Neural Network (DNN) model in an electronic device. The method includes: estimating, by the electronic device, an activation range of each layer of the DNN model using self-generated data (e.g. retro image, audio, video, etc.) and/or a sensitive index of each layer of the DNN model; quantizing, by the electronic device, the DNN model based on the activation range and/or the sensitive index; and allocating, by the electronic device, a dynamic bit precision for each channel of each layer of the DNN model to quantize the DNN model.

公开(公告)号：US20240135181A1

公开(公告)日：2024-04-25

申请号：US18541972

申请日：2023-12-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Gokulkrishna M ,  Siva Kailash SACHITHANANDAM ,  Prasanna R ,  Rajath Elias SOANS ,  Alladi Ashok Kumar SENAPATI ,  Praveen Doreswamy NAIDU ,  Pradeep NELAHONNE SHIVAMURTHAPPA

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: A method for validating a trained artificial intelligence (AI) model on a device is provided. The method includes deploying a validation model generated by applying a plurality of anticipated configurational changes associated with the trained AI model requiring validation. Further, the method includes providing input data to each of the validation model and the trained AI model for receiving an output from each of the validation model and the trained AI model, wherein the output of the validation model is further based on one or more actual configurational deviations that occurred during training of the trained AI model since deployment of the trained AI model on the device. Furthermore, the method includes combining the output of each of the validation model and the trained AI model to validate the trained AI model.

公开(公告)号：US20230252756A1

公开(公告)日：2023-08-10

申请号：US18172774

申请日：2023-02-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Rajath Elias SOANS ,  Pradeep NELAHONNE SHIVAMURTHAPPA ,  Kuladeep MARUPALLI ,  Alladi Ashok Kumar SENAPATI ,  Ananya PAUL

IPC: G06V10/26 ,  G06V10/25 ,  G06V10/82

CPC classification number: G06V10/267 ,  G06V10/25 ,  G06V10/82

Abstract: A method for processing an input frame for an on-device AI model is provided. The method may include obtaining an input frame. The method may include building at least one kernel independent of the scale of the input frame by passing input variables to the at least one kernel using preprocessor directives independent of the scale of the input frame. The method may include inputting the input frame to the on-device AI model including the at least one kernel independent of the scale of the input frame. The method may include processing the input frame in the on-device AI model.