Convolution layers are prevalent in many classes of deep neural networks,
including Convolutional Neural Networks (CNNs) which provide state-of-the-art
results for tasks like image recognition, neural machine translation and speech
recognition. The computationally expensive nature of a convolution operation
has led to the proliferation of implementations including matrix-matrix
multiplication formulation, and direct convolution primarily targeting GPUs. In
this paper, we introduce direct convolution kernels for x86 architectures, in
particular for Xeon and XeonPhi systems, which are implemented via a dynamic
compilation approach. Our JIT-based implementation shows close to theoretical
peak performance, depending on the setting and the CPU architecture at hand. We
additionally demonstrate how these JIT-optimized kernels can be integrated into
a lightweight multi-node graph execution model. This illustrates that single-
and multi-node runs yield high efficiencies and high image-throughputs when
executing state-of-the-art image recognition tasks on CPUs.

本文介绍了针对 x86 体系结构的直接卷积核和动态编译方法实现的 JIT 优化内核，该内核可在多节点下高效执行最新的图像识别任务，使单机和多节点运行时高效地通过 CPU 执行任务的高吞吐量。