MEC: Memory-efficient Convolution for Deep Neural Network
This work addresses memory inefficiency in convolution for deep learning practitioners, offering an incremental improvement over existing indirect methods.
The paper tackles the high memory overhead of indirect convolution algorithms in deep neural networks by proposing MEC, a memory-efficient convolution method with compact lowering, which substantially reduces memory consumption and accelerates convolution, achieving significant memory reduction and good speedup on mobile and server platforms.
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.