公开(公告)号：US20230153565A1

公开(公告)日：2023-05-18

申请号：US17430644

申请日：2021-07-09

Applicant: SAMSUNG ELECTRONICS CO., LTD

Inventor： Brijraj SINGH ,  Mayukh DAS ,  Yash Hemant JAIN ,  Sharan Kumar ALLUR ,  Venkappa MALA ,  Praveen Doreswamy NAIDU

IPC: G06N3/04 ,  G06N3/092

CPC classification number: G06N3/04 ,  G06N3/092

Abstract: A method of deep neural network (DNN) modularization for optimal loading includes receiving, by an electronic device, a DNN model for execution, obtaining, by the electronic device, a plurality of parameters associated with the electronic device and a plurality of parameters associated with the DNN model, determining, by the electronic device, a number of sub-models of the DNN model and a splitting index, based on the obtained plurality of parameters associated with the electronic device and the obtained plurality of parameters associated with the DNN model, and splitting, by the electronic device, the received DNN model into a plurality of sub-models, based on the determined number of sub-models of the DNN model and the determined splitting index.

公开(公告)号：US20200019854A1

公开(公告)日：2020-01-16

申请号：US16480545

申请日：2018-02-23

Applicant: Samsung Electronics Co., Ltd.

Inventor： Arun ABRAHAM ,  Suhas Parlathaya KUDRAL ,  Balaji Srinivas HOLUR ,  Sarbojit GANGULY ,  Venkappa MALA ,  Suneel Kumar SURIMANI ,  Sharan Kumar ALLUR

IPC: G06N3/08 ,  G06F9/48 ,  G06F9/445

Abstract: The present invention describes a method of accelerating execution of one or more application tasks in a computing device using machine learning (ML) based model. According to one embodiment, a neural accelerating engine present in the computing device receives a ML input task for execution on the computing device from a user. The neural accelerating engine further retrieves a trained ML model and a corresponding optimal configuration file based on the received ML input task. Also, the current performance status of the computing device for executing the ML input task is obtained. Then, the neural accelerating engine dynamically schedules and dispatches parts of the ML input task to one or more processing units in the computing device for execution based on the retrieved optimal configuration file and the obtained current performance status of the computing device.

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：US20220066829A1

公开(公告)日：2022-03-03

申请号：US17453945

申请日：2021-11-08

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Ashutosh PAVAGADA VISWESWARA ,  Pallavi THUMMALA ,  Chirag GIRDHAR ,  Alladi Ashok Kumar SENAPATI ,  Pradeep N S NELAHONNE SHIVAMURTHAPPA ,  Venkappa MALA

IPC: G06F9/50 ,  G06F9/445

Abstract: Disclosed herein is a method and an optimization unit for optimizing and/or improving efficiency of resource utilization in an embedded computing system executing Artificial Intelligence (AI) applications. The method includes: detecting, by an optimization unit comprising processing circuitry and/or executable program instructions configured in the embedded computing system, a launch of an AI application on the embedded computing system; retrieving a runtime profile corresponding to the AI application, the runtime profile indicating resource requirements for executing the AI application; and configuring a runtime environment of the embedded computing system for the AI application based on the runtime profile corresponding to the AI application.

公开(公告)号：US20240232587A1

公开(公告)日：2024-07-11

申请号：US18429690

申请日：2024-02-01

Applicant: Samsung Electronics Co., Ltd.

Inventor： Srinivas Soumitri MIRIYALA ,  Efthymia TSAMOURA ,  Shah Ayub QUADRI ,  Vikram Nelvoy RAJENDIRAN ,  Venkappa MALA

IPC: G06N3/047 ,  G06N7/01

CPC classification number: G06N3/047 ,  G06N7/01

Abstract: A method and an electronic device for neuro-symbolic learning of an artificial intelligence (AI) model are provided. The method includes receiving input data including various contents and determining in an output of the AI model a predicted probability for each of the contents of the input data, determining a neural loss of the AI model by comparing the predicted probability with a predefined desired probability, determining a symbolic loss for the AI model by comparing the predicted probability with a pre-determined undesired probability, determining weights of a plurality of layers of the AI model, and updating the weights of the plurality of layers of the AI model based on the neural loss and the symbolic loss.

公开(公告)号：US20210232921A1

公开(公告)日：2021-07-29

申请号：US17159598

申请日：2021-01-27

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Akshay PARASHAR ,  Arun ABRAHAM ,  Payal ANAND ,  Deepthy RAVI ,  Venkappa MALA ,  Vikram Nelvoy RAJENDIRAN

IPC: G06N3/08 ,  G06N3/063 ,  G06F9/30 ,  G06F9/38 ,  G06F9/48

Abstract: A method, an apparatus, and a system for configuring a neural network across heterogeneous processors are provided. The method includes creating a unified neural network profile for the plurality of processors; receiving at least one request to perform at least one task using the neural network; determining a type of the requested at least one task as one of an asynchronous task and a synchronous task; and parallelizing processing of the neural network across the plurality of processors to perform the requested at least one task, based on the type of the requested at least one task and the created unified neural network profile.