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 · Edit social preview As neural network model sizes have dramatically increased, so has the interest in various...
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。
Convolutional Neural Networks (CNNs) achieve state-of-the art performance in a wide range of applications including image recognition, speech recognition, and natural language processing. Large-scale CNNs generally have encountered limitations in computing and storage resources, but sparse CNNs have eme...
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,优点是直接减少不重要的通道数,缺点是会对下游层产生影响,因此要reconstruction 文章的核心内容是对训练好的模型进行通道剪枝(channel...
[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,...
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。
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!
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。
Accelerating deep neural networks (DNN) inference is an important step in realizing latency-critical deployment of real-world applications such as image classification, image segmentation, natural language processing, and so on. The need for improving DNN inference latency has sparked interest in running...