The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network
Sparse3Dconvolutionalneuralnetworks BenGraham UniversityofWarwick b.graham@warwick.ac.uk May12,2015 Abstract Wehaveimplementedaconvolutionalneuralnetworkdesignedforpro- cessingsparsethree-dimensionalinputdata.Theworldweliveinisthree dimensionalsotherearealargenumberofpotentialapplications.Inthe questforefficiency,we...
Convolutional Neural Networks(2):Sparse Interactions, Receptive Field and Parameter Sharing Sparse Interactions, Receptive Field and Parameter Sharing是整个CNN深度网络的核心部分,我们用本文来具体分析其原理。 首先我们考虑Feedforward Neural Network,L层的输出矩阵,等于L层的输入矩阵与L层的权重矩阵做矩阵乘法,而...
Anyone has interest to utilize the sparsity to accelerate DNNs? I am working on the fork https://github.com/wenwei202/caffe/tree/scnn and currently, on average, achieve ~5x CPU and ~3x GPU layer-wise speedups of convolutional layers in A...
Caffe for Sparse Convolutional Neural Network. Contribute to IntelLabs/SkimCaffe development by creating an account on GitHub.
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels,the product of structured spar...
SPARSE CONVOLUTIONAL NEURAL NETWORK 专利名称：SPARSE CONVOLUTIONAL NEURAL NETWORK 发明人：ZHANG, Chen,LIU, Yunxin 申请号：US2020/030327 申请日：20200429 公开号：WO2020/256836A1 公开日：20201224 专利内容由知识产权出版社提供 专利附图：摘要：Various implementations of the subject matter as described herein...
The contribution of the proposed block-sparse convolutional neural network (BSCNN) are mainly three-fold: (1) Use of sparse factors for kernels, which allow sparse blocks to be randomly obtained based on certain scaling factor pairs to form a new convolution kernel. This new kernel has the sa...
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks 方法介绍 目的: 探索稀疏性和预测精度之间的关系 能不能在稀疏性的规则中找到一个平衡点,这个点是如何提升硬件实现效率的 为了探索上面的问题,文章设置了几种不同的剪枝粒度等级,在前人的基础上进行对比实验,探索剪枝粒度和预测精度之间的...
FIG. 1 illustrates a flowchart of a method for performing computations using a Sparse Convolutional Neural Network (SCNN) Accelerator, in accordance with one embodiment; FIG. 2A illustrates a block diagram of a SCNN accelerator, in accordance with one embodiment; FIG. 2B illustrates a conceptual ...