公开(公告)号：US20240273335A1

公开(公告)日：2024-08-15

申请号：US18582487

申请日：2024-02-20

Applicant: Apple Inc.

Inventor： Saman NADERIPARIZI ,  Mohammad RASTEGARI ,  Sayyed Karen KHATAMIFARD

IPC: G06N3/02 ,  G06F3/06 ,  G06N3/045 ,  G06N3/063

CPC classification number: G06N3/02 ,  G06F3/0604 ,  G06F3/0676 ,  G06F3/0677 ,  G06N3/045 ,  G06N3/063

Abstract: In one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (PLD) implementing a convolutional neural network (CNN), wherein: each compute block of the PLD corresponds to one of a multiple of convolutional layers of the CNN, each compute block of the PLD is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the CNN, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the CNN.

公开(公告)号：US20220222550A1

公开(公告)日：2022-07-14

申请号：US17583133

申请日：2022-01-24

Applicant: Apple Inc.

Inventor： Alexander James Oscar Craver KIRCHHOFF ,  Ali FARHADI ,  Anish Jnyaneshwar PRABHU ,  Carlo Eduardo Cabanero DEL MUNDO ,  Daniel Carl TORMOEN ,  Hessam BAGHERINEZHAD ,  Matthew S. WEAVER ,  Maxwell Christian HORTON ,  Mohammad RASTEGARI ,  Robert Stephen KARL, JR. ,  Sophie LEBRECHT

IPC: G06N5/04 ,  G06F11/34 ,  H04N5/232 ,  G06F8/41 ,  G06F8/10

Abstract: In one embodiment, a method includes providing, to a client system of a user, a user interface for display. The user interface may include a first set of options for selecting an artificial intelligence (AI) task for integrating into a user application, a second set of options for selecting one or more devices on which the user wants to deploy the selected AI task, and a third set of options for selecting one or more performance constraints specific to the selected devices. User specifications may be received based on user selections in the first, second, and third sets of options. A custom AI model may be generated based on the user specifications and sent to the client system of the user for integrating into the user application. The custom AI model once integrated may enable the user application to perform the selected AI task on the selected devices.

公开(公告)号：US20250037018A1

公开(公告)日：2025-01-30

申请号：US18658919

申请日：2024-05-08

Applicant: Apple Inc.

Inventor： Minsik CHO ,  Keivan ALIZADEH VAHID ,  Qichen FU ,  Saurabh ADYA ,  Carlo Eduardo Cabanero DEL MUNDO ,  Mohammad RASTEGARI ,  Devang K. NAIK ,  Peter ZATLOUKAL

IPC: G06N20/00

Abstract: The subject technology provides memory-efficient differentiable weight clustering for large language model compression. An apparatus determines a tensor including an attention map between learned weights of a trained machine learning model and corresponding centroids. The apparatus also determines a compressed attention table and a plurality of index lists during compression of the trained machine learning model based on an uniquification of the attention map and sharding of an associated index list. The apparatus determines whether the tensor exists at a destination device during compression of the trained machine learning model using a marshaling layer. The apparatus refrains from copying the tensor to the destination device when the tensor exists at the destination device, or copies the tensor to the destination device when the tensor does not exist at the destination device. The apparatus deploys a compressed machine learning model based on the compression of the trained machine learning model.

公开(公告)号：US20240144566A1

公开(公告)日：2024-05-02

申请号：US18544288

申请日：2023-12-18

Applicant: Apple Inc.

Inventor： Hessam BAGHERINEZHAD ,  Maxwell HORTON ,  Mohammad RASTEGARI ,  Ali FARHADI

IPC: G06T11/60 ,  G06F18/214 ,  G06F18/241 ,  G06N3/045 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10

CPC classification number: G06T11/60 ,  G06F18/2148 ,  G06F18/241 ,  G06N3/045 ,  G06N3/08 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10 ,  G06T2210/22 ,  G06V20/68

Abstract: Systems and methods are disclosed for training neural networks using labels for training data that are dynamically refined using neural networks and using these trained neural networks to perform detection and/or classification of one or more objects appearing in an image. Particular embodiments may generate a set of crops of images from a corpus of images, then apply a first neural network to the set of crops to obtain a set of respective outputs. A second neural network may then be trained using the set of crops as training examples. The set of respective outputs may be applied as labels for the set of crops.

公开(公告)号：US20220343135A1

公开(公告)日：2022-10-27

申请号：US17860031

申请日：2022-07-07

Applicant: Apple Inc.

Inventor： Saman NADERIPARIZI ,  Mohammad RASTEGARI ,  Sayyed Karen KHATAMIFARD

IPC: G06N3/02 ,  G06F3/06 ,  G06N3/04

Abstract: In one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (PLD) implementing a convolutional neural network (CNN), wherein: each compute block of the PLD corresponds to one of a multiple of convolutional layers of the CNN, each compute block of the PLD is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the CNN, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the CNN.