CN112106034A - 一种用于神经网络的卷积方法及装置 - Google Patents
一种用于神经网络的卷积方法及装置 Download PDFInfo
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Abstract
本申请实施例提供一种用于神经网络的卷积方法及装置,涉及计算机技术领域,实现避免深度学习过程中的无效操作,以节约处理计算能力以及节省数据带宽。具体包括:对原始数据采样,以获取至少一个采样数据和该原始数据中除采样数据以外的至少一个剩余数据,剩余数据包括待处理剩余数据;当该参考采样数据与该卷积核的卷积结果不满足预设条件时,将该待处理剩余数据与卷积核的卷积结果设置为0。本申请用于卷积计算。
Description
PCT国内申请,说明书已公开。
Claims (15)
- PCT国内申请,权利要求书已公开。
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PCT/CN2018/095713 WO2020010639A1 (zh) | 2018-07-13 | 2018-07-13 | 一种用于神经网络的卷积方法及装置 |
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CN112106034A true CN112106034A (zh) | 2020-12-18 |
CN112106034B CN112106034B (zh) | 2024-05-24 |
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Citations (7)
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US9552510B2 (en) * | 2015-03-18 | 2017-01-24 | Adobe Systems Incorporated | Facial expression capture for character animation |
US10762894B2 (en) * | 2015-03-27 | 2020-09-01 | Google Llc | Convolutional neural networks |
CN104915322B (zh) * | 2015-06-09 | 2018-05-01 | 中国人民解放军国防科学技术大学 | 一种卷积神经网络硬件加速方法 |
CN105260776B (zh) * | 2015-09-10 | 2018-03-27 | 华为技术有限公司 | 神经网络处理器和卷积神经网络处理器 |
CN107995628A (zh) * | 2017-12-18 | 2018-05-04 | 北京工业大学 | 一种深度学习的认知无线网络多用户协作频谱感知方法 |
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- 2018-07-13 WO PCT/CN2018/095713 patent/WO2020010639A1/zh active Application Filing
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CN103384836A (zh) * | 2010-12-22 | 2013-11-06 | 皇家飞利浦电子股份有限公司 | 包含grappa算子的用于任意k空间轨迹的快速两步并行重建 |
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CN106326985A (zh) * | 2016-08-18 | 2017-01-11 | 北京旷视科技有限公司 | 神经网络训练方法和装置及数据处理方法和装置 |
WO2018120740A1 (zh) * | 2016-12-29 | 2018-07-05 | 深圳光启合众科技有限公司 | 图片分类方法和装置、机器人 |
CN106971230A (zh) * | 2017-05-10 | 2017-07-21 | 中国石油大学(北京) | 基于深度学习的初至拾取方法及装置 |
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Title |
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CN112106034B (zh) | 2024-05-24 |
WO2020010639A1 (zh) | 2020-01-16 |
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