Convolution is a critical component in modern deep neural networks, thus
several algorithms for convolution have been developed. Direct convolution is
simple but suffers from poor performance. As an alternative, multiple indirect
methods have been proposed including im2col-based convolution, FFT-based
convolution, or Winograd-based algorithm. However, all these indirect methods
have high memory-overhead, which creates performance degradation and offers a
poor trade-off between performance and memory consumption. In this work, we
propose a memory-efficient convolution or MEC with compact lowering, which
reduces memory-overhead substantially and accelerates convolution process. MEC
lowers the input matrix in a simple yet efficient/compact way (i.e., much less
memory-overhead), and then executes multiple small matrix multiplications in
parallel to get convolution completed. Additionally, the reduced memory
footprint improves memory sub-system efficiency, improving performance. Our
experimental results show that MEC reduces memory consumption significantly
with good speedup on both mobile and server platforms, compared with other
indirect convolution algorithms.

本文提出一种具有内存效率的卷积方法，即 MEC，使用简单且高效 / 紧凑的方式降低输入矩阵的内存开销，提高卷积运算速度，显著减少内存消耗，在移动设备和服务器平台上都具有良好的加速效果，适用于卷积运算的深度神经网络模型。