公开(公告)号：US20250094535A1

公开(公告)日：2025-03-20

申请号：US18469424

申请日：2023-09-18

Applicant: QUALCOMM Incorporated

Inventor： Shivansh RAO ,  Sweta PRIYADARSHI ,  Varun RAVI KUMAR ,  Senthil Kumar YOGAMANI ,  Arunkumar NEHRUR RAVI ,  Vasudev BHASKARAN

IPC: G06F18/213 ,  H04W4/38 ,  H04W4/46

Abstract: According to aspects described herein, a device can extract first features from frames of first sensor data and second features from frames of second sensor data (captured after the first sensor data). The device can obtain first weighted features based on the first features and second weighted features based on the second features. The device can aggregate the first weighted features to determine a first feature vector and the second weighted features to determine a second feature vector. The device can obtain a first transformed feature vector (based on transforming the first feature vector into a coordinate space) and a second transformed feature vector (based on transforming the second feature vector into the coordinate space). The device can aggregate first transformed weighted features (based on the first transformed feature vector) and second transformed weighted features (based on the second transformed feature vector) to determine a fused feature vector.

公开(公告)号：US20250148358A1

公开(公告)日：2025-05-08

申请号：US18504117

申请日：2023-11-07

Applicant: QUALCOMM Incorporated

Inventor： Zhuojin LI ,  Hsin-Pai CHENG ,  Hong CAI ,  Sweta PRIYADARSHI ,  Kartikeya BHARDWAJ ,  Viswanath GANAPATHY ,  Chirag Sureshbhai PATEL ,  Fatih Murat PORIKLI

IPC: G06N20/00

Abstract: A processor-implemented method for training-free architecture searching for a transformer model includes generating a set of transformer model candidates for a target device. Each transformer model candidate of the set of transformer model candidates is initialized with random weights. A set of data samples are randomly sampled to produce random data samples for inputting at each transformer model candidate. An attention confidence score is computed for each transformer model candidate based on the random data samples and the random weights. A transformer model candidate for the target device is selected based on the attention confidence score.