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System and methods for efficiently implementing a convolutional neural network incorporating binarized filter and convolution operation for performing image classification

机译:用于有效实现结合了二值化滤波器和卷积运算以进行图像分类的卷积神经网络的系统和方法

摘要

Systems, apparatuses, and methods for efficiently and accurately processing an image in order to detect and identify one or more objects contained in the image, and methods that may be implemented on mobile or other resource constrained devices. Embodiments of the invention introduce simple, efficient, and accurate approximations to the functions performed by a convolutional neural network (CNN); this is achieved by binarization (i.e., converting one form of data to binary values) of the weights and of the intermediate representations of data in a convolutional neural network. The inventive binarization methods include optimization processes that determine the best approximations of the convolution operations that are part of implementing a CNN using binary operations.
机译:用于有效和准确地处理图像以便检测和识别图像中包含的一个或多个对象的系统,装置和方法,以及可以在移动或其他资源受限的设备上实现的方法。本发明的实施例对由卷积神经网络(CNN)执行的功能引入简单,有效和准确的近似。这是通过在卷积神经网络中对权重和数据的中间表示进行二值化(即,将一种形式的数据转换为二进制值)来实现的。本发明的二值化方法包括确定卷积运算的最佳近似的优化过程,这是使用二进制运算实现CNN的一部分。

著录项

  • 公开/公告号US10311342B1

    专利类型

  • 公开/公告日2019-06-04

    原文格式PDF

  • 申请/专利权人 XNOR.AI INC.;

    申请/专利号US201715487091

  • 发明设计人 ALI FARHADI;MOHAMMAD RASTEGARI;

    申请日2017-04-13

  • 分类号G06K9;G06T1/40;G06K9/66;G06N3/04;G06K9/62;G06K9/52;

  • 国家 US

  • 入库时间 2022-08-21 12:12:15

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