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    Determining layer ranks for compression of deep networks
    1.
    发明授权
    Determining layer ranks for compression of deep networks 有权

    公开(公告)号：US11586924B2

    公开(公告)日：2023-02-21

    申请号：US15877723

    申请日：2018-01-23

    Applicant: QUALCOMM Incorporated

    Inventor： Anthony Sarah ,  Raghuraman Krishnamoorthi

    IPC: G06N3/08 ,  G06N3/084 ,  G06N3/04

    Abstract: An apparatus of operating a computational network is configured to determine a low-rank approximation for one or more layers of the computational network based at least in part on a set of residual targets. A set of candidate rank vectors corresponding to the set of residual targets may be determined. Each of the candidate rank vectors may be evaluated using an objective function. A candidate rank vector may be selected and used to determine the low rank approximation. The computational network may be compressed based on the low-rank approximation. In turn the computational network may be operated using the one or more compressed layers.

    Methods and apparatus for modulating the training of a neural device
    4.
    发明授权
    Methods and apparatus for modulating the training of a neural device 有权
    用于调制神经元装置训练的方法和装置

    公开(公告)号：US09542644B2

    公开(公告)日：2017-01-10

    申请号：US14079181

    申请日：2013-11-13

    Applicant: QUALCOMM Incorporated

    Inventor： Michael-David Nakayoshi Canoy ,  Yinyin Liu ,  Anthony Sarah ,  Adrienne Milner

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/049

    Abstract: Methods and apparatus are provided for training a neural device having an artificial nervous system by modulating at least one training parameter during the training. One example method for training a neural device having an artificial nervous system generally includes observing the neural device in a training environment and modulating at least one training parameter based at least in part on the observing. For example, the training apparatus described herein may modify the neural device's internal learning mechanisms (e.g., spike rate, learning rate, neuromodulators, sensor sensitivity, etc.) and/or the training environment's stimuli (e.g., move a flame closer to the device, make the scene darker, etc.). In this manner, the speed with which the neural device is trained (i.e., the training rate) may be significantly increased compared to conventional neural device training systems.

    Abstract translation: 提供了用于通过在训练期间调制至少一个训练参数来训练具有人造神经系统的神经装置的方法和装置。 用于训练具有人造神经系统的神经装置的一个示例性方法通常包括在训练环境中观察神经装置并且至少部分地基于观察来调制至少一个训练参数。 例如，本文描述的训练装置可以修改神经装置的内部学习机制（例如，尖峰率，学习速率，神经调节器，传感器灵敏度等）和/或训练环境的刺激（例如，将火焰移动到设备附近 ，使场景更暗等）。 以这种方式，与传统的神经元装置训练系统相比，神经装置训练的速度（即，训练速率）可以显着增加。

    Dynamically assigning and examining synaptic delay
    6.
    发明授权
    Dynamically assigning and examining synaptic delay 有权
    动态分配和检查突触延迟

    公开(公告)号：US09536190B2

    公开(公告)日：2017-01-03

    申请号：US14056856

    申请日：2013-10-17

    Applicant: QUALCOMM Incorporated

    Inventor： Anthony Sarah ,  Robert Howard Kimball ,  Brian Spinar

    IPC: G06F15/18 ,  G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/049

    Abstract: A method for dynamically modifying synaptic delays in a neural network includes initializing a delay parameter and operating the neural network. The method further includes dynamically updating the delay parameter based on a program which is based on a statement including the delay parameter.

    Abstract translation: 用于动态修改神经网络中的突触延迟的方法包括初始化延迟参数并操作神经网络。 该方法还包括基于基于包括延迟参数的语句的程序来动态地更新延迟参数。

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