We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs). It has been shown that there is a redundancy in standard CNNs, as networks with light or sparse convolution operators yield similar performance to full networks. However, the number of parameters in the former networks also scales quadratically in width, while in the latter case, the parameters typically have random sparsity patterns, hampering hardware efficiency. Our approach for building CNN architectures scales linearly with respect to the network's width while retaining full coupling of the channels as in standard CNNs. To this end, we replace each convolution block with its MGIC block utilizing a hierarchy of lightweight convolutions. Our extensive experiments on image classification, segmentation, and point cloud classification show that applying this strategy to different architectures like ResNet and MobileNetV3 considerably reduces the number of parameters while obtaining similar or better accuracy. For example, we obtain 76.1% top-1 accuracy on ImageNet with a lightweight network with similar parameters and FLOPs to MobileNetV3.

翻译：我们提出了一个多电网内通道(MGIC)方法,解决标准电动神经神经网络(CNN)中频道数量参数数的四倍增长问题。已经表明,标准CNN系统重复了标准CNN系统,因为光线或稀疏电动操作器网络的性能与整个网络类似。然而,前网络中的参数数量也以宽度成比例,而在后一种情况下,参数通常有随机的散射模式,妨碍硬件效率。我们用来在网络宽度方面建立CNN结构的线性尺度,同时保留标准CNN系统那样的频道全面连接。为此,我们利用轻量级共变的等级,用MGIC组替换每个组合块。我们在图像分类、分解和点云分类方面的广泛实验表明,将这一战略应用到ResNet和MmovedNet3等不同结构,大大降低了参数的数量,同时获得了类似或更好的精确度。例如,我们在图像网络上获得76.1%的上上端1级精确度,并有类似的参数和FOPLL3。