公开(公告)号：US20160234757A1

公开(公告)日：2016-08-11

申请号：US14620146

申请日：2015-02-11

Applicant: QUALCOMM Incorporated

Inventor： Kiran Kumar SOMASUNDARAM ,  Nachiappan VALLIAPPAN ,  Naga BHUSHAN ,  Dexu LIN ,  Ahmed Kamel SADEK

IPC: H04W48/08 ,  H04W12/10 ,  H04W16/14

CPC classification number: H04W48/08 ,  H04L63/0823 ,  H04W12/00516 ,  H04W12/10 ,  H04W16/14 ,  H04W48/16 ,  H04W84/12 ,  H04W88/10

Abstract: The present disclosure presents a method and an apparatus for transmitting discovery signaling from a base station. For example, the method may include encoding a wireless fidelity (Wi-Fi) beacon at the base station for transmission and transmitting the encoded Wi-Fi beacon from the base station to one or more neighboring wireless nodes. The Wi-Fi beacon is generated by a Wi-Fi access point (AP) co-located at the base station which is a long term evolution (LTE) or LTE advanced in unlicensed spectrum base station. As such, other wireless nodes can discover the LTE or LTE advanced in unlicensed spectrum base station.

Abstract translation: 本公开提供了一种用于从基站发送发现信令的方法和装置。 例如，该方法可以包括在基站处对无线保真（Wi-Fi）信标进行编码，以便传输，并将编码的Wi-Fi信标从基站发射到一个或多个相邻的无线节点。 Wi-Fi信标由位于基站的Wi-Fi接入点（AP）产生，该基站是长期演进（LTE）或未授权频谱基站中的LTE。 因此，其他无线节点可以在未许可频谱基站中发现LTE或LTE高级。

公开(公告)号：US20170039469A1

公开(公告)日：2017-02-09

申请号：US14849518

申请日：2015-09-09

Applicant: QUALCOMM Incorporated

Inventor： Somdeb MAJUMDAR ,  Dexu LIN ,  Regan Blythe TOWAL ,  Anthony SARAH

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06K9/4628 ,  G06K9/6256 ,  G06K9/6269 ,  G06N3/0445 ,  G06N3/0454 ,  G06N20/00

Abstract: A method of detecting unknown classes is presented and includes generating a first classifier for multiple first classes. In one configuration, an output of the first classifier has a dimension of at least two. The method also includes designing a second classifier to receive the output of the first classifier to decide whether input data belongs to the multiple first classes or at least one second class.

Abstract translation: 提出了一种检测未知类的方法，包括生成多个第一类的第一分类器。 在一个配置中，第一分类器的输出具有至少两个维度。 该方法还包括设计第二分类器以接收第一分类器的输出以确定输入数据是属于多个第一类还是至少一个第二类。

公开(公告)号：US20180060278A1

公开(公告)日：2018-03-01

申请号：US15255015

申请日：2016-09-01

Applicant: QUALCOMM Incorporated

Inventor： Dexu LIN ,  Edward LIAO ,  Somdeb MAJUMDAR ,  Aaron LAMB ,  Karamvir CHATHA

IPC: G06F17/17

CPC classification number: G06F17/17 ,  G06F7/544 ,  G06F2207/5354

Abstract: Computing a non-linear function ƒ(x) in hardware or embedded systems can be complex and resource intensive. In one or more aspects of the disclosure, a method, a computer-readable medium, and an apparatus are provided for computing a non-linear function ƒ(x) accurately and efficiently in hardware using look-up tables (LUTs) and interpolation or extrapolation. The apparatus may be a processor. The processor computes a non-linear function ƒ(x) for an input variable x, where ƒ(x)=g(y(x),z(x)). The processor determines an integer n by determining a position of a most significant bit (MSB) of an input variable x. In addition, the processor determines a value for y(x) based on a first look-up table and the determined integer n. Also, the processor determines a value for z(x) based on n and the input variable x, and based on a second look-up table. Further, the processor computes ƒ(x) based on the determined values for y(x) and z(x).

公开(公告)号：US20160328645A1

公开(公告)日：2016-11-10

申请号：US14882351

申请日：2015-10-13

Applicant: QUALCOMM Incorporated

Inventor： Dexu LIN ,  Matthew BADIN ,  David Edward HOWARD ,  Daniel Hendricus Franciscus DIJKMAN ,  Michael Colin TREMAINE ,  Anthony SARAH

IPC: G06N3/08 ,  G06N99/00

CPC classification number: G06N3/08 ,  G06N3/063 ,  G06N20/00

Abstract: A method of reducing computational complexity for a fixed point neural network operating in a system having a limited bit width in a multiplier-accumulator (MAC) includes reducing a number of bit shift operations when computing activations in the fixed point neural network. The method also includes balancing an amount of quantization error and an overflow error when computing activations in the fixed point neural network.

Abstract translation: 一种降低在乘法器 - 累加器（MAC）中具有有限位宽度的系统中操作的固定点神经网络的计算复杂度的方法包括在计算固定点神经网络中的激活时减少多个位移操作。 该方法还包括在计算固定点神经网络中的激活时平衡量化误差量和溢出误差。

公开(公告)号：US20160328647A1

公开(公告)日：2016-11-10

申请号：US14936594

申请日：2015-11-09

Applicant: QUALCOMM Incorporated

Inventor： Dexu LIN ,  Venkata Sreekanta Reddy ANNAPUREDDY ,  David Jonathan JULIAN ,  Casimir Matthew WIERZYNSKI

IPC: G06N3/08 ,  G06F17/11

CPC classification number: G06N3/08 ,  G06F17/11 ,  G06N3/063 ,  G06N3/10

Abstract: A method for selecting bit widths for a fixed point machine learning model includes evaluating a sensitivity of model accuracy to bit widths at each computational stage of the model. The method also includes selecting a bit width for parameters, and/or intermediate calculations in the computational stages of the mode. The bit width for the parameters and the bit width for the intermediate calculations may be different. The selected bit width may be determined based on the sensitivity evaluation.

Abstract translation: 用于选择固定点机器学习模型的位宽度的方法包括在模型的每个计算阶段评估模型精度对位宽度的灵敏度。 该方法还包括在模式的计算阶段中选择参数的位宽度和/或中间计算。 参数的位宽和中间计算的位宽可能不同。 可以基于灵敏度评估来确定所选择的位宽度。

公开(公告)号：US20160328646A1

公开(公告)日：2016-11-10

申请号：US14920099

申请日：2015-10-22

Applicant: QUALCOMM Incorporated

Inventor： Dexu LIN ,  Venkata Sreekanta Reddy ANNAPUREDDY ,  David Edward HOWARD ,  David Jonathan JULIAN ,  Somdeb MAJUMDAR ,  William Richard BELL, II

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06K9/4628 ,  G06N3/04 ,  G06N3/06 ,  G06N3/10

Abstract: A method of quantizing a floating point machine learning network to obtain a fixed point machine learning network using a quantizer may include selecting at least one moment of an input distribution of the floating point machine learning network. The method may also include determining quantizer parameters for quantizing values of the floating point machine learning network based at least in part on the at least one selected moment of the input distribution of the floating point machine learning network to obtain corresponding values of the fixed point machine learning network.

Abstract translation: 使用量化器来量化浮点计算机学习网络以获得定点机器学习网络的方法可以包括选择浮点计算机学习网络的输入分布的至少一个时刻。 该方法还可以包括：至少部分地基于浮点机器学习网络的输入分布的至少一个选定时刻来确定用于量化浮点计算机学习网络的值的量化器参数，以获得固定点计算机的相应值 学习网络

公开(公告)号：US20160328644A1

公开(公告)日：2016-11-10

申请号：US14878689

申请日：2015-10-08

Applicant: QUALCOMM Incorporated

Inventor： Dexu LIN ,  Venkata Sreekanta Reddy ANNAPUREDDY ,  Sachin Subhash TALATHI ,  Mark STASKAUSKAS ,  Aniket VARTAK ,  Regan Blythe TOWAL ,  David Jonathan JULIAN ,  Anthony SARAH

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06K9/4628 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/084

Abstract: A method of adaptively selecting a configuration for a machine learning process includes determining current system resources and performance specifications of a current system. A new configuration for the machine learning process is determined based at least in part on the current system resources and the performance specifications. The method also includes dynamically selecting between a current configuration and the new configuration based at least in part on the current system resources and the performance specifications.

Abstract translation: 自动选择用于机器学习过程的配置的方法包括确定当前系统的当前系统资源和性能规范。 至少部分地基于当前系统资源和性能规格确定用于机器学习过程的新配置。 该方法还包括至少部分地基于当前系统资源和性能规范来动态地在当前配置和新配置之间进行选择。