加速卷积神经网络网络

如何用FPGA加速卷积神经网络(CNN)？

性能瓶颈也主要在于卷积时需要大量乘加运算，参与计算的大量weight参数会带来的很多访存请求。接下来考察下前人的工作和当前的灌水热点。按理说这种大量的乘加运算用dsp应该不错，但是在cnn中大家并不需要这么大的位宽，有时候8位就够了。...

使用专用硬件加速深度卷积神经网络

每次移动，3D卷积核的每个权值同映射到3D输入立方体的输入值进行乘加，卷积后，可选地进行pooling操作（由参数p和s定义）用来下采样卷积输出，选择窗口的最大值或平均值【在数据中心加速深度卷积神经网络】2014年微软宣布了Catapult项目，...

CNNdroid：在 Android 上利用 GPU 加速执行 CNN(卷积神经网络)

图1:CNN在移动设备上的应用许多基于深度学习的硬件加速平台都有相应的解决方案了，IBM也正在开发一种用于神经网络的CMOS的芯片，用于在移动设备和物联网设备上。与此同时，类似的解决方案依然处于早期的研发阶段，也并没有商用到现有的...

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