为了使稀疏切实可行,Nvidia Ampere GPU在Tensor Core中引入了对稀疏的支持。论文描述了sparse tensor的核心设计和行为: 利用2:4 稀疏(每4个元素的block中至少有2个零, 即稀疏度 50%), 相比dense matrix multiply可以获得一倍的性能提升。同时论文还介绍了一种训练稀疏神经网络的流程: 同时满足 2:4 稀疏和精度不...
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...
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。
tensor factorization :mobilenet 为代表,作者提出这种方法无法分解1*1的卷积,而1*1的卷积普遍用在GoogleNet ,ResNet 和Xception 上 sparse connection:以deep compression 为代表,这种方法缺点是被pruning 后的连接和神经元会形成不规则的结构,不容易在硬件上进行配置实现 channel pruning:本文采用的方法,优点是直接减少...
论文:Channel Pruning for Accelerating Very Deep Neural Networks 论文链接:https://arxiv.org/abs/1707.06168 代码地址:https://github.com/yihui-he/channel-pruning 这是一篇ICCV2017的文章,关于用通道剪枝(channel pruning)来做模型加速,通道减枝是模型压缩和加速领域的一个重要分支。
[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,...
a symmetric, non-sparse distribution, that is “more Gaussian” (Hyv¨arinen & Oja, 2000); normalizing it is likely to produce activations with a stable distribution. 注意:加入BN层之后,Wu+ b中的Bias可以去掉了,原因如下: b的作用被beta取代了!
Deep compression and EIE: Efficient inference engine on compressed deep neural network Presents a collection of slides covering the following topics: sparse DNN; CPU; GPU; linear algebra; and deep learning. H Song,X Liu,H Mao,... - IEEE 被引量: 446发表: 2017年 Core Fusion: Accommodating ...
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...
In our method, unlike more familiar uses of deep neural networks, retraining of the model is required for each graph layout as the training process is the optimization of the FDL which needs to be performed for every new graph layout. As we show next, the proposed GNN-based method improves...