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在英特尔® 数学核心函数库中引入 DNN 基元

    深度神经网络 (DNN) 处于机器学习领域的前沿。这些算法在 20 世纪 90 年代后期得到了行业的广泛采用,最初应用于诸如银行支票手写识别等任务。深度神经网络在这一任务领域已得到广泛运用,达到甚至超过了人类能力。如今,DNN 已用于图像识别、视频和自然语言处理以及解决复杂的视觉理解问题,如自主驾驶等。DNN 在计算资源及其必须处理的数据量方面要求非常苛刻。就这一点而言,现代图像识别拓扑 AlexNet 需要几天的时间在现代计算系统中进行训练,并使用超过 1,400 万张图像。消除这种复杂性需要使用经过良好优化的构建模块来缩短训练时间,以满足工业应用的需求。

    英特尔® 数学核心函数库(英特尔® MKL)2017 引入了 DNN 域,它包含加速最常用的图像识别拓扑(包括 AlexNet、VGG、GoogleNet 和 ResNet)所需的功能。

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