快速卷积以及矩阵乘法

1. 【算法+图像处理】2D卷积与快速卷积算法C语言实现

https://blog.csdn.net/guduruyu/article/details/78385418?utm_source=blogxgwz0

用“ 快速卷积”来加速‘卷积’ （不同放大倍率的VDSR 重建程序）https://blog.csdn.net/juebai123/article/details/80960654?utm_source=blogxgwz2

2. 一种快速卷积实现方法（FFT变换）

https://blog.csdn.net/B1009/article/details/78922764?utm_source=blogxgwz4

利用FFT实现快速卷积实验https://download.csdn.net/download/qq_27975371/8800473

挺有意思的一篇扫盲文，当年学校，深有同感：卷积与傅里叶变换https://blog.csdn.net/Augusdi/article/details/12438011?utm_source=blogxgwz2

3. 高效卷积算法（综述总结性）

https://blog.csdn.net/fupotui7870/article/details/79946990?utm_source=blogxgwz0

4. More is Less——卷积网络加速

https://blog.csdn.net/shuzfan/article/details/70172346?utm_source=blogxgwz0

5. C++的矩阵乘向量加速trick（循环展开）

https://blog.csdn.net/jacke121/article/details/65440754?utm_source=blogxgwz8

6. MEC —— 优化内存与速度的卷积计算

https://blog.csdn.net/shuzfan/article/details/77427979?utm_source=blogxgwz6

7.【算法导论】矩阵乘法strassen算法

CPU上的算法复杂度从3次方到2.8次方

https://blog.csdn.net/zhuangxiaobin/article/details/36476769?utm_source=blogxgwz0

8.Sparse-Winograd CNN——权重剪枝与Winograd的结合

https://blog.csdn.net/nature553863/article/details/80307277?utm_source=blogxgwz2

Paper地址：https://arxiv.org/abs/1802.06367

GitHub地址：https://github.com/xingyul/Sparse-Winograd-CNN

AI芯片：商汤科技基于winograd算法的FPGA方案分析

https://blog.csdn.net/evolone/article/details/80136193?utm_source=blogxgwz0

用FPGA评估卷积神经网络快速算法

https://blog.csdn.net/XingpengLu/article/details/80919665?utm_source=blogxgwz6

fpga 实现cnn

https://github.com/doonny/PipeCNN/tree/master/documents

如何简单快捷地用小型Xiliinx FPGA加速卷积神经网络CNN

https://blog.csdn.net/awai54st/article/details/78177351?utm_source=blogxgwz1

https://github.com/awai54st/PYNQ-Classification

 

C++性能优化技术导论

https://blog.csdn.net/zhangxinrun/article/details/7634999