公开(公告)号：US20250086431A1

公开(公告)日：2025-03-13

申请号：US18462872

申请日：2023-09-07

Applicant: QUALCOMM Incorporated

Inventor： Pierre-David LETOURNEAU ,  Dalton James JONES ,  Matthew James MORSE ,  Matthew Harper LANGSTON

IPC: G06N3/0455 ,  G06F17/16 ,  G06N3/0464

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for executing a workload on a computing device based on an approximation of a target function. An example method generally includes generating a plurality of sample points from a multi-dimensional space representing domain of a target function. A plurality of sparse matrices is generated from the plurality of sample points, each respective sparse matrix being generated based on known non-zero coefficients of the target function. A plurality of transformed sparse matrices representing a relationship between Chebyshev coefficients of the target function and the plurality of sample points after applying lower dimensional cosine transformations are generated. An approximation of the target function is generated based on the plurality of transformed sparse matrices. An output of at least a portion of a neural network may be generated based on the approximation of the target function.

公开(公告)号：US20240354363A1

公开(公告)日：2024-10-24

申请号：US18639845

申请日：2024-04-18

Applicant: QUALCOMM Incorporated

Inventor： Pierre-David LETOURNEAU ,  Rania HASSEN ,  Gary MCGRATH ,  Matthew Harper LANGSTON

IPC: G06F17/11

CPC classification number: G06F17/11

Abstract: Methods, systems, and media for solving quadratic optimization problems are disclosed herein. In some embodiments, a method may involve receiving, by one or more processors, a first quadratic optimization problem comprising an objective and a set of inequality constraints. The method may involve obtaining an initial solution to the first quadratic optimization problem subject to the set of inequality constraints. The method may involve identifying a subset of the set of inequality constraints that are active constraints with respect to an optimal solution. The method may involve obtaining an updated solution to the first quadratic optimization problem by solving a second quadratic optimization problem that corresponds to optimizing the objective subject to the active constraints. The method may involve determining an accuracy and precision associated with the updated solution.