LEARNED STEP SIZE QUANTIZATION
    1.
    发明申请

    公开(公告)号:US20210264279A1

    公开(公告)日:2021-08-26

    申请号:US16796397

    申请日:2020-02-20

    IPC分类号: G06N3/08 G06F17/16 G06N3/04

    摘要: Learned step size quantization in artificial neural network is provided. In various embodiments, a system comprises an artificial neural network and a computing node. The artificial neural network comprises: a quantizer having a configurable step size, the quantizer adapted to receive a plurality of input values and quantize the plurality of input values according to the configurable step size to produce a plurality of quantized input values, at least one matrix multiplier configured to receive the plurality of quantized input values from the quantizer and to apply a plurality of weights to the quantized input values to determine a plurality of output values having a first precision, and a multiplier configured to scale the output values to a second precision. The computing node is operatively coupled to the artificial neural network and is configured to: provide training input data to the artificial neural network, and optimize the configurable step size based on a gradient through the quantizer and the training input data.

    Transform for a neurosynaptic core circuit

    公开(公告)号:US10832121B2

    公开(公告)日:2020-11-10

    申请号:US16391092

    申请日:2019-04-22

    摘要: Embodiments of the present invention provide a method for feature extraction comprising generating synaptic connectivity information for a neurosynaptic core circuit. The core circuit comprises one or more electronic neurons, one or more electronic axons, and an interconnect fabric including a plurality of synapse devices for interconnecting the neurons with the axons. The method further comprises initializing the interconnect fabric based on the synaptic connectivity information generated, and extracting a set of features from input received via the electronic axons. The set of features extracted comprises a set of features with reduced correlation.

    Transform for a neurosynaptic core circuit

    公开(公告)号:US10318862B2

    公开(公告)日:2019-06-11

    申请号:US15980612

    申请日:2018-05-15

    IPC分类号: G06N3/063 G06N3/08

    摘要: Embodiments of the present invention provide a method for feature extraction comprising generating synaptic connectivity information for a neurosynaptic core circuit. The core circuit comprises one or more electronic neurons, one or more electronic axons, and an interconnect fabric including a plurality of synapse devices for interconnecting the neurons with the axons. The method further comprises initializing the interconnect fabric based on the synaptic connectivity information generated, and extracting a set of features from input received via the electronic axons. The set of features extracted comprises a set of features with reduced correlation.

    TRANSFORM FOR A NEUROSYNAPTIC CORE CIRCUIT

    公开(公告)号:US20170213125A1

    公开(公告)日:2017-07-27

    申请号:US15184892

    申请日:2016-06-16

    IPC分类号: G06N3/04

    摘要: Embodiments of the present invention provide a method for feature extraction comprising generating synaptic connectivity information for a neurosynaptic core circuit. The core circuit comprises one or more electronic neurons, one or more electronic axons, and an interconnect fabric including a plurality of synapse devices for interconnecting the neurons with the axons. The method further comprises initializing the interconnect fabric based on the synaptic connectivity information generated, and extracting a set of features from input received via the electronic axons. The set of features extracted comprises a set of features with reduced correlation.