# MEC: Memory-efficient Convolution for Deep Neural Network

arXiv: https://arxiv.org/abs/1706.06873 · HTML: https://mlsanity.com/paper/1706.06873/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2017.

## Built on by (most cited first, 14 of 14)

- [RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition](https://mlsanity.com/paper/2105.01883/) (2021)
- [RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality](https://mlsanity.com/paper/2112.11081/) (2021)
- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [High Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2104.08314/) (2021)
- [Advancing Direct Convolution using Convolution Slicing Optimization and ISA Extensions](https://mlsanity.com/paper/2303.04739/) (2023)
- [Im2win: An Efficient Convolution Paradigm on GPU](https://mlsanity.com/paper/2306.14316/) (2023)
- [Im2win: Memory Efficient Convolution On SIMD Architectures](https://mlsanity.com/paper/2306.14320/) (2023)
- [AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities](https://mlsanity.com/paper/2502.05315/) (2025)
- [High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures](https://mlsanity.com/paper/2408.00278/) (2024)
- [Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores](https://mlsanity.com/paper/2608.20725/) (2026)
- [Modulating Regularization Frequency for Efficient Compression-Aware Model Training](https://mlsanity.com/paper/2105.01875/) (2021)
- [Towards a learning-based performance modeling for accelerating Deep Neural Networks](https://mlsanity.com/paper/2212.05031/) (2022)
- [SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution](https://mlsanity.com/paper/2411.15659/) (2024)
- [MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference](https://mlsanity.com/paper/2609.28358/) (2026)

## Abstract (arXiv)

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.