mlsanityData to 14 Oct 2026

SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution

arXiv 2411.15659 ↗First posted 23 Nov 2024cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks: (a) im2col requires a large memory buffer and can experience inefficient memory access, and (b) while GEMM is highly optimized for scientific matrices multiplications, it is not well suited for convolutions. We propose an approach that takes advantage of scalar-matrix multiplication and reduces memory overhead. Our experiments with commonly used network architectures demonstrate a significant speedup compared to existing indirect methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 23 Nov 2024.

Builds on

12 of 27 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 288
  5. 2014Speeding up Convolutional Neural Networks with Low Rank Expansionscited by 284
  6. 2020Binary Neural Networks: A Surveycited by 190
  7. 2015Fast Algorithms for Convolutional Neural Networkscited by 102
  8. 2018YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computerscited by 34
  9. 2017MEC: Memory-efficient Convolution for Deep Neural Networkcited by 14
  10. 2018High Performance Zero-Memory Overhead Direct Convolutionscited by 14
  11. 2022Hypernetwork-Based Adaptive Image Restorationcited by 8
  12. 2020The Role of Redundant Bases and Shrinkage Functions in Image Denoisingcited by 2

Built on by

Not cited yet.