公开(公告)号：US20210287095A1

公开(公告)日：2021-09-16

申请号：US16816117

申请日：2020-03-11

Applicant: QUALCOMM Incorporated

Inventor： Jamie Menjay LIN ,  Edwin Chongwoo PARK ,  Nojun KWAK

IPC: G06N3/08 ,  G06N5/04 ,  G06N20/10 ,  G06F7/523

Abstract: A method for operating a low-bitwidth neural network includes converting a first activation to a non-negative value (e.g., absolute value). The first activation has a signed value. The sign of the activation is used to select a weight value. A product of the non-negative activation and the selected weight value is computed to determine a next activation. The next activation is quantized and supplied to a subsequent layer of the low-bitwidth neural network.

公开(公告)号：US20220065981A1

公开(公告)日：2022-03-03

申请号：US17461516

申请日：2021-08-30

Applicant: QUALCOMM Incorporated

Inventor： Jamie Menjay LIN ,  Nojun KWAK ,  Fatih Murat PORIKLI

IPC: G01S5/02 ,  G06N20/00 ,  H04W4/029

Abstract: Certain aspects of the present disclosure provide techniques for machine learning using basis decomposition, comprising receiving a first runtime record, where the first runtime record includes RF signal data collected in a physical space; processing the first runtime record using a plurality of basis machine learning (ML) models to generate a plurality of inferences; aggregating the plurality of inferences to generate a prediction comprising a plurality of coordinates; and outputting the prediction, where the plurality of coordinates indicate a location of a physical element in a physical space.

公开(公告)号：US20210150347A1

公开(公告)日：2021-05-20

申请号：US17098159

申请日：2020-11-13

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mange BORSE ,  Nojun KWAK ,  Daniel Hendricus Franciscus DIJKMAN ,  Bence MAJOR

IPC: G06N3/08 ,  G06N3/063 ,  G06N3/04 ,  G06K9/62

Abstract: Aspects described herein provide a method of performing guided training of a neural network model, including: receiving supplementary domain feature data; providing the supplementary domain feature data to a fully connected layer of a neural network model; receiving from the fully connected layer supplementary domain feature scaling data; providing the supplementary domain feature scaling data to an activation function; receiving from the activation function supplementary domain feature weight data; receiving a set of feature maps from a first convolution layer of the neural network model; fusing the supplementary domain feature weight data with the set of feature maps to form fused feature maps; and providing the fused feature maps to a second convolution layer of the neural network model.