The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust mo...The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To improve the accuracy of core tensor coding,the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in image.When applied to MS-RSI restoration,our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.展开更多
Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.Th...Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.The multichannel audio is represented with 3-order tensor space and is decomposed into core tensor with three factor matrices in the way of channel,time and frequency.Only the truncated core tensor is transmitted which will be multiplied by the pre-trained factor matrices to reconstruct the original tensor space.Objective and subjective experiments have been done to show a very noticeable compression capability with an acceptable output quality.The novelty of the proposed compression method is that it enables both high compression capability and backward compatibility with limited signal distortion to the hearing.展开更多
We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can condu...We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.展开更多
张量转置(tensor transposition)作为基础张量运算原语,广泛应用于信号处理、科学计算以及深度学习等各种领域,在张量数据密集型应用及高性能计算中具有重要作用。随着能效指标在高性能计算系统中的重要性日益凸显,基于数字信号处理器(d...张量转置(tensor transposition)作为基础张量运算原语,广泛应用于信号处理、科学计算以及深度学习等各种领域,在张量数据密集型应用及高性能计算中具有重要作用。随着能效指标在高性能计算系统中的重要性日益凸显,基于数字信号处理器(digital signal processors,DSPs)的加速器已被集成至通用计算系统。然而,传统面向多核CPU和GPU的张量转置库因架构差异无法充分适配DSP架构。一方面,DSP架构的向量化计算潜力尚未得到充分挖掘;另一方面,其复杂的片上存储体系与多层次共享内存结构为张量并行程序设计带来了显著挑战。针对国产多核DSP的架构特点,提出ftmTT算法,并设计实现了一个面向多核DSP架构的通用张量转置库。ftmTT算法通过设计适配DSP架构的高效内存访问模式充分挖掘其并行化和向量化潜力,其核心创新包括:1)采用分块策略将高维张量转置转化为多核DSP平台所提供的矩阵转置内核操作;2)提出基于DMA点对点传输的张量数据块访存合并方案来降低数据搬运开销;3)通过双缓冲设计异步重叠转置计算与DMA传输实现计算通信隐藏,最终面向多核DSP实现高性能并行张量转置。在国产多核DSP平台FT-M7032的实验表明,ftmTT张量转置算法取得了最高达理论带宽75.96%的性能,达到FT-M7032平台STREAM带宽99.23%的性能。展开更多
基金This work was supported by the Project of Shandong Province Higher Educational Science and Technology Program[KJ2018BAN047,Geng,L.]National Natural Science Foundation of China[61801222,Fu,P.]+2 种基金Fundamental Research Funds for the Central Universities[30919011230,Fu,P.]Science and Technology Innovation Program for Distributed Young Talents of Shandong Province Higher Education Institutions[2019KJN045,Guo,Q.]Shandong Provincial Key Laboratory of Network Based Intelligent Computing[http://nbic.ujn.edu.cn/].
文摘The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To improve the accuracy of core tensor coding,the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in image.When applied to MS-RSI restoration,our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.
基金This work was partially supported by the National Natural Science Foundation of China under Grants No.11161140319,No.61001188，the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20101101110020，the Fund for Basic Research from Beijing Institute of Technology under Grant No.20120542011，the Fund for Beijing Higher Education Young Elite Teacher Project under Grant No.YETP1202
文摘Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.The multichannel audio is represented with 3-order tensor space and is decomposed into core tensor with three factor matrices in the way of channel,time and frequency.Only the truncated core tensor is transmitted which will be multiplied by the pre-trained factor matrices to reconstruct the original tensor space.Objective and subjective experiments have been done to show a very noticeable compression capability with an acceptable output quality.The novelty of the proposed compression method is that it enables both high compression capability and backward compatibility with limited signal distortion to the hearing.
基金supported by the National Key R&D Program of China (No.2019YFB2203700)the National Natural Science Foundation of China (No.61822508)。
文摘We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.
文摘张量转置(tensor transposition)作为基础张量运算原语,广泛应用于信号处理、科学计算以及深度学习等各种领域,在张量数据密集型应用及高性能计算中具有重要作用。随着能效指标在高性能计算系统中的重要性日益凸显,基于数字信号处理器(digital signal processors,DSPs)的加速器已被集成至通用计算系统。然而,传统面向多核CPU和GPU的张量转置库因架构差异无法充分适配DSP架构。一方面,DSP架构的向量化计算潜力尚未得到充分挖掘;另一方面,其复杂的片上存储体系与多层次共享内存结构为张量并行程序设计带来了显著挑战。针对国产多核DSP的架构特点,提出ftmTT算法,并设计实现了一个面向多核DSP架构的通用张量转置库。ftmTT算法通过设计适配DSP架构的高效内存访问模式充分挖掘其并行化和向量化潜力,其核心创新包括:1)采用分块策略将高维张量转置转化为多核DSP平台所提供的矩阵转置内核操作;2)提出基于DMA点对点传输的张量数据块访存合并方案来降低数据搬运开销;3)通过双缓冲设计异步重叠转置计算与DMA传输实现计算通信隐藏,最终面向多核DSP实现高性能并行张量转置。在国产多核DSP平台FT-M7032的实验表明,ftmTT张量转置算法取得了最高达理论带宽75.96%的性能,达到FT-M7032平台STREAM带宽99.23%的性能。
