Convolution is the core component within deep neural networks and it is computationally intensive and time consuming. Tensor data layouts significantly impact convolution operations in terms of memory access and computational efficiency. Yet, there is still a lack of comprehensive performance characterization on data layouts on SIMD architectures concerning convolution methods. This paper proposes three novel data layouts for im2win convolution: NHWC, CHWN, and CHWN8, and introduces a set of general optimization techniques for both direct and im2win convolutions. We compare the optimized im2win convolution with the direct convolution and PyTorch's im2col-based convolution across the aforementioned layouts on SIMD machines. The experiments demonstrated that the im2win convolution with the new NHWC layout achieved up to 355% performance speedup over NCHW layout. Our optimizations also significantly improve the performance of both im2win and direct convolutions. Our optimized im2win and direct convolutions achieved up to 95% and 94% of machine's theoretical peak performance, respectively.

该研究解决了在SIMD架构上卷积操作性能不足的问题，提出了三种新颖的张量数据布局：NHWC、CHWN和CHWN8，并引入了通用的优化技术。实验结果显示，新的NHWC布局使得im2win卷积的性能提升达355%，同时优化后的卷积方法显著提高了整体性能，有望推动深度学习模型的计算效率。

基于三种张量布局在SIMD架构上的高性能Im2win和直接卷积