公开(公告)号：US20240428576A1

公开(公告)日：2024-12-26

申请号：US18613263

申请日：2024-03-22

Applicant: QUALCOMM Incorporated

Inventor： Tianyu JIANG ,  Manish Kumar SINGH ,  Hsin-Pai CHENG ,  Hong CAI ,  Mingu LEE ,  Kartikeya BHARDWAJ ,  Christopher LOTT ,  Fatih Murat PORIKLI

IPC: G06V10/82 ,  G06V10/70 ,  G06V10/77

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A transformed version of image pixels is accessed as input to an attention layer of a machine learning model. A number of local attention operations to apply, in one transformer, to the transformed version of image pixels is selected based at least in part on a size of the transformed version of image pixels. A transformer output for the attention layer of the machine learning model is generated based on applying the number of local attention operations and at least one global attention operation to the transformed version of image pixels.

公开(公告)号：US20240249128A1

公开(公告)日：2024-07-25

申请号：US18353637

申请日：2023-07-17

Applicant: QUALCOMM Incorporated

Inventor： Burak BARTAN ,  Edward TEAGUE ,  Christopher LOTT

IPC: G06N3/063

CPC classification number: G06N3/063

Abstract: A processor-implemented method for rematerialization for an artificial neural network (ANN) includes receiving a graph representing the ANN. The graph includes multiple nodes connected by edges and each node represents an operation. Retention intervals for the nodes are determined based on a precedence constraint for the nodes. The retention intervals correspond to a time interval for retaining each node output in a local memory. One of the nodes to recompute is determined based on the retention intervals.

公开(公告)号：US20220335929A1

公开(公告)日：2022-10-20

申请号：US17700378

申请日：2022-03-21

Applicant: QUALCOMM Incorporated

Inventor： Yang YANG ,  Anusha Lalitha ,  Jin Won LEE ,  Christopher LOTT

IPC: G10L15/187 ,  G10L15/06 ,  G10L15/22 ,  G10L15/19 ,  G10L15/07 ,  G06F3/16 ,  G10L15/02

Abstract: Various embodiments include methods and devices for implementing automatic grammar augmentation for improving voice command recognition accuracy in systems with a small footprint acoustic model. Alternative expressions that may capture acoustic model decoding variations may be added to a grammar set. An acoustic model-specific statistical pronunciation dictionary may be derived by running the acoustic model through a large general speech dataset and constructing a command-specific candidate set containing potential grammar expressions. Greedy based and cross-entropy-method (CEM) based algorithms may be utilized to search the candidate set for augmentations with improved recognition accuracy.

公开(公告)号：US20170157769A1

公开(公告)日：2017-06-08

申请号：US15192719

申请日：2016-06-24

Applicant: QUALCOMM Incorporated

Inventor： Aliakbar AGHAMOHAMMADI ,  Bardia Fallah BEHABADI ,  Christopher LOTT ,  Shayegan OMIDSHAFIEI ,  Kiran SOMASUNDARAM ,  Sarah Paige GIBSON ,  Casimir Matthew WIERZYNSKI ,  Saurav AGARWAL ,  Gerhard REITMAYR ,  Serafin DIAZ

IPC: B25J9/16

CPC classification number: B25J9/1664 ,  G01C21/20 ,  G01C21/32 ,  G05B2219/40512 ,  G05B2219/40519 ,  G05D1/0217 ,  G05D1/0251 ,  G05D1/0274 ,  Y10S901/01

Abstract: A method substantially simultaneously plans a path and maps an environment by a robot. The method determines a mean of an occupancy level for a location in a map. The method also includes determining a probability distribution function (PDF) of the occupancy level. The method further includes calculating a cost function based on the PDF. Finally, the method includes simultaneously planning the path and mapping the environment based on the cost function.

公开(公告)号：US20250021761A1

公开(公告)日：2025-01-16

申请号：US18545804

申请日：2023-12-19

Applicant: QUALCOMM Incorporated

Inventor： Arvind Vardarajan SANTHANAM ,  Joseph Binamira SORIAGA ,  Roland MEMISEVIC ,  Mingu LEE ,  Christopher LOTT

IPC: G06F40/284

Abstract: Techniques and apparatus for generating a response to a query input into a generative artificial intelligence model. An example method generally includes generating, based on an input query and a first generative artificial intelligence model, a sequence of tokens corresponding to a candidate response to the input query. The sequence of tokens and the input query are output to a second generative artificial intelligence model for verification. One or more first guidance signals for the generated sequence of tokens are received from the second generative artificial intelligence model. The candidate response to the input query is revised based on the generated sequence of tokens and the one or more first guidance signals, and the revised candidate response is output as a response to the received input query.

公开(公告)号：US20240386237A1

公开(公告)日：2024-11-21

申请号：US18495609

申请日：2023-10-26

Applicant: QUALCOMM Incorporated

Inventor： Burak BARTAN ,  Edward TEAGUE ,  Christopher LOTT

IPC: G06N3/02

Abstract: A processor-implemented method includes receiving a graph representing an artificial neural network (ANN). The graph includes multiple nodes connected by edges and each node represents an operation. Retention intervals are determined for the multiple node outputs based on rematerialization constraints and paging constraints. The retention intervals correspond to a time interval for retaining each node output in at least one local memory. A sequence of tasks for executing the multiple nodes of the graph representing the ANN is determined based on the retention intervals.

公开(公告)号：US20240119301A1

公开(公告)日：2024-04-11

申请号：US18464996

申请日：2023-09-11

Applicant: QUALCOMM Incorporated

Inventor： Wonseok JEON ,  Mukul GAGRANI ,  Weiliang ZENG ,  Edward TEAGUE ,  Burak BARTAN ,  Piero ZAPPI ,  Christopher LOTT

IPC: G06N3/092

CPC classification number: G06N3/092

Abstract: A processor-implemented method includes sampling, according to a priority sampling policy, a set of node priorities from a computation graph. Each node priority of the set of node priorities may be associated with a respective node on the computation graph. Additionally, each node may represent an operation of a task performed by an artificial neural network. The method also includes converting, via a list scheduling function, the node priorities to a schedule that associates each node of the computation graph with a processor of a group of processors of a device associated with the artificial neural network, the schedule associated with a makespan. The method further includes performing the task in accordance with the schedule.

公开(公告)号：US20170161910A1

公开(公告)日：2017-06-08

申请号：US15192603

申请日：2016-06-24

Applicant: QUALCOMM Incorporated

Inventor： Aliakbar AGHAMOHAMMADI ,  Saurav AGARWAL ,  Shayegan OMIDSHAFIEI ,  Kiran SOMASUNDARAM ,  Christopher LOTT ,  Bardia Fallah BEHABADI ,  Sarah Paige GIBSON ,  Casimir Matthew WIERZYNSKI ,  Gerhard REITMAYR ,  Serafin DIAZ

IPC: G06T7/20 ,  G05D1/02 ,  G06K9/62

CPC classification number: G05D1/0274 ,  G05D1/0251 ,  G06K9/00664 ,  G06K9/6277 ,  G06T7/579 ,  G06T2207/10021 ,  G06T2207/20076 ,  G06T2207/30252

Abstract: A method for defining a sensor model includes determining a probability of obtaining a measurement from multiple potential causes in a field of view of a sensor modeled based on a stochastic map. The stochastic map includes a mean occupancy level for each voxel in the stochastic map and a variance of the mean occupancy level for each pixel. The method also includes determining a probability of obtaining an image based on the determined probability of obtaining the measurement. The method further includes planning an action for a robot, comprising the sensor, based on the probability of obtaining the image.

公开(公告)号：US20250124265A1

公开(公告)日：2025-04-17

申请号：US18544169

申请日：2023-12-18

Applicant: QUALCOMM Incorporated

Inventor： Mingu LEE ,  Kanghwan JANG ,  Christopher LOTT ,  Liang ZHANG

IPC: G06N3/048

Abstract: A processor-implemented method determines a practical domain for a following function in a following layer of an artificial neural network. The artificial neural network includes a leading function in a leading layer and the following function in the following layer, which is a subsequent consecutive layer of the artificial neural network. The method also sets a first quantization range of an output activation of the leading function based on the practical domain.

公开(公告)号：US20230376851A1

公开(公告)日：2023-11-23

申请号：US18319259

申请日：2023-05-17

Applicant: QUALCOMM Incorporated

Inventor： Mingu LEE ,  Saurabh Kedar PITRE ,  Tianyu JIANG ,  Christopher LOTT

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for performing machine learning. In one example, an input data sequence is accessed, and the input data sequence is sliced based on a slice length hyperparameter to generate a stacked slice input data representation. The stacked slice input data representation is processed with a slice attention layer to generate a stacked slice output data representation. The stacked slice output data representation is de-sliced to generate an output data sequence.