Accelerating Sparse Deep Neural Networks 术语 本文中的同义词 稀疏 和 sparsity 裁剪 和 pruning 摘要 随着神经网络规模的急剧增大, 人们引入了很多种技术用来减小模型大小, 加速模型推理。稀疏是其中一个比较活跃的研究领域: 利用 tensor 中的零值, 我们可以节省空间, 加速计算。虽然大多数研究都集中在 high-level...
[1] J. M. Alvarez and M. Salzmann. Learning the number of neurons in deep networks. In Advances in Neural Informa- tion Processing Systems, pages 2262–2270, 2016. [3] S. Anwar and W. Sung. Compact deep convolutional neural networks with coarse pruning. arXiv preprint arXiv:1610.09639,...
本文采用的通道剪枝(channel pruning)是模型压缩和加速领域中一种简化网络结构的操作,文中作者还列举了其他两种常见的简化网络结构的操作:sparse connection和tensor factorization,可以看Figure1的对比。(a)表示传统的3层卷积操作。(b)表示sparse connection,这是通过去掉一些参数很小的连接得到的,理论上是有明显的加速效...
tensor factorization 就是将矩阵分解 sparse connection 就是让网络连接变得稀疏 channel pruning 信道裁剪 channel pruning 方法 first k selects the first k channels. 这种方法太简单粗暴了。 max response 也就是选择权值和最大的信道,认为拥有的信息最多。
The increasing interest in filter pruning of convolutional neural networks stems from its inherent ability to effectively compress and accelerate these networks. Currently, filter pruning is mainly divided into two schools: norm-based and relation-based. These methods aim to selectively remove the least...
We propose a new mechanism, which we call Batch Normalization, that takes a step towards reducing internal covariate shift, and in doing so dramatically accelerates the training of deep neural nets. It accomplishes this via a normalization step that fixes themeans and variances of layer inputs....
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 1. 摘要 训练深层的神经网络非常困难,因为在训练的过程中,随着前面层数参数的改变,每层输入的分布也会随之改变。这需要我们设置较小的学习率并且谨慎地对参数进行初始化,因此训练过程比较缓慢。
PDF全文链接:论文解读Channel pruning for Accelerating Very Deep Neural Networks 本文提出了一种新的裁枝方法,用于加速深层卷积神经网络。对于一个训练好的模型,本文方法通过一个2步迭代的算法逐层裁枝,优化函数是LASSO回归和最小二乘法重建误差。进一步,本文将算法推广到多层的裁枝,和多分枝网络的裁枝。结果上,本...
prune_agent = IterRecoverFramework(pack, GBNs, sparse_lambda = cfg.gbn.sparse_lambda, flops_eta = cfg.gbn.flops_eta, minium_filter =3) 1)准备好了Tick-Tock # 先所有数据迭代cfg.gbn.tock_epoch次 prune_agent.tock(lr_min=cfg.gbn.lr_min, lr_max=cfg.gbn.lr_max, tock_epoch=cfg.gbn.to...
Accelerating Sparse Deep Neural Networks 16 Apr 2021·Asit Mishra,Jorge Albericio Latorre,Jeff Pool,Darko Stosic,Dusan Stosic,Ganesh Venkatesh,Chong Yu,Paulius Micikevicius· As neural network model sizes have dramatically increased, so has the interest in various techniques to reduce their parameter...