In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations t...In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations to achieve flexible switching between different requirements when implemented in hardware.To address this problem,this paper proposes a matrix operation accelerator based on reconfigurable arrays in the context of the application of recommender systems(RS).Based on the reconfigurable array processor(APR-16)with reconfiguration,a parallelized design of matrix operations on processing element(PE)array is realized with flexibility.The experimental results show that,compared with the proposed central processing unit(CPU)and graphics processing unit(GPU)hybrid implementation matrix multiplication framework,the energy efficiency ratio of the accelerator proposed in this paper is improved by about 35×.Compared with blocked alternating least squares(BALS),its the energy efficiency ratio has been accelerated by about 1×,and the switching of matrix factorization(MF)schemes suitable for different sparsity can be realized.展开更多
构建基于激光雷达(light detection and ranging,LiDAR)数据的林分平均高反演模型可为无瓣海桑长势的动态监测提供技术支撑。以北部湾茅尾海无瓣海桑红树林湿地为对象,基于机载LiDAR提取的高度和强度参数变量,借助决定系数R^(2)、均方...构建基于激光雷达(light detection and ranging,LiDAR)数据的林分平均高反演模型可为无瓣海桑长势的动态监测提供技术支撑。以北部湾茅尾海无瓣海桑红树林湿地为对象,基于机载LiDAR提取的高度和强度参数变量,借助决定系数R^(2)、均方根误差RMSE、赤池信息准则AIC和贝叶斯信息准则BIC指标对随机森林、支持向量机以及神经网络3种模型进行了优选,在最优模型的支持下估算了研究区的红树林平均高及其空间分布状况。结果表明,研究区无瓣海桑的林分平均高介于3.90~11.58 m之间,其中树高较高、胸径较大的无瓣海桑主要分布在潮沟附近以及研究区中部。在估算无瓣海桑的林分平均高时,贡献率最大的是样方点云高度最大值,其次是75%~99%分位数高度。随机森林回归模型在估测林分平均高模型中的精度最高(R^(2)=0.9381,RMSE=0.58 m,AIC=80.50和BIC=49.05);支持向量机模型次之,该模型在测试阶段的R^(2)为0.7665,RMSE为1.27 m;神经网络模型的拟合效果最差。总体而言,随机森林模型是研究区无瓣海桑林分平均高反演的最优模型。展开更多
基金the National Key R&D Program of China(No.2022ZD0119001)the National Natural Science Foundation of China(No.61834005)+3 种基金the Shaanxi Province Key R&D Plan(No.2022GY-027)the Key Scientific Research Project of Shaanxi Department of Education(No.22JY060)the Education Research Project of Xi'an University of Posts and Telecommunications(No.JGA202108)the Graduate Student Innovation Fund of Xi’an University of Posts and Telecommunications(No.CXJJYL2022035).
文摘In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations to achieve flexible switching between different requirements when implemented in hardware.To address this problem,this paper proposes a matrix operation accelerator based on reconfigurable arrays in the context of the application of recommender systems(RS).Based on the reconfigurable array processor(APR-16)with reconfiguration,a parallelized design of matrix operations on processing element(PE)array is realized with flexibility.The experimental results show that,compared with the proposed central processing unit(CPU)and graphics processing unit(GPU)hybrid implementation matrix multiplication framework,the energy efficiency ratio of the accelerator proposed in this paper is improved by about 35×.Compared with blocked alternating least squares(BALS),its the energy efficiency ratio has been accelerated by about 1×,and the switching of matrix factorization(MF)schemes suitable for different sparsity can be realized.
