# Recent Advances in Convolutional Neural Network Acceleration

arXiv: https://arxiv.org/abs/1807.08596 · HTML: https://mlsanity.com/paper/1807.08596/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 17 of 17)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial](https://mlsanity.com/paper/2108.11663/) (2021)
- [Parallel Algorithms Align with Neural Execution](https://mlsanity.com/paper/2307.04049/) (2023)
- [Mixed X-Ray Image Separation for Artworks with Concealed Designs](https://mlsanity.com/paper/2201.09167/) (2022)
- [Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery](https://mlsanity.com/paper/2209.13500/) (2022)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings](https://mlsanity.com/paper/2106.06688/) (2021)
- [A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks](https://mlsanity.com/paper/2107.09095/) (2021)
- [T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator](https://mlsanity.com/paper/2306.04240/) (2023)
- [FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2505.13461/) (2025)
- [Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems](https://mlsanity.com/paper/2107.09101/) (2021)
- [Challenges and Solutions in DeepFakes](https://mlsanity.com/paper/2109.05397/) (2021)
- [Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine](https://mlsanity.com/paper/2209.09168/) (2022)
- [A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection](https://mlsanity.com/paper/2304.04005/) (2023)
- [A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection](https://mlsanity.com/paper/2305.09407/) (2023)
- [Phasor-Driven Acceleration for FFT-based CNNs](https://mlsanity.com/paper/2406.00290/) (2024)
- [Entity-Aware and Secure Query Optimization in Database Using Named Entity Recognition](https://mlsanity.com/paper/2601.01254/) (2026)

## Abstract (arXiv)

In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN's further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e.~structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration.