This research reveals the dependency of floating point computation in nonlinear dynamical systems on machine precision and step-size by applying a multiple-precision approach in the Lorenz nonlinear equations. The pap...This research reveals the dependency of floating point computation in nonlinear dynamical systems on machine precision and step-size by applying a multiple-precision approach in the Lorenz nonlinear equations. The paper also demoastrates the procedures for obtaining a real numerical solution in the Lorenz system with long-time integration and a new multiple-precision-based approach used to identify the maximum effective computation time （MECT） and optimal step-size （OS）. In addition, the authors introduce how to analyze round-off error in a long-time integration in some typical cases of nonlinear systems and present its approximate estimate expression.展开更多
Deep learning algorithms are the basis of many artificial intelligence applications.Those algorithms are both computationally intensive and memory intensive,making them difficult to deploy on embedded systems.Thus var...Deep learning algorithms are the basis of many artificial intelligence applications.Those algorithms are both computationally intensive and memory intensive,making them difficult to deploy on embedded systems.Thus various deep learning accelerators(DLAs)are proposed and applied to achieve better performance and lower power consumption.However,most deep learning accelerators are unable to support multiple data formats.This research proposes the MW-DLA,a deep learning accelerator supporting dynamic configurable data-width.This work analyzes the data distribution of different data types in different layers and trains a typical network with per-layer representation.As a result,the proposed MW-DLA achieves 2X performance and more than 50%memory requirement for AlexNet with less than 5.77%area overhead.展开更多
基金This study was supported by the National Key Basic Research and Development Project of China 2004CB418303 the National Natural Science foundation of China under Grant Nos. 40305012 and 40475027Jiangsu Key Laboratory of Meteorological Disaster KLME0601.
文摘This research reveals the dependency of floating point computation in nonlinear dynamical systems on machine precision and step-size by applying a multiple-precision approach in the Lorenz nonlinear equations. The paper also demoastrates the procedures for obtaining a real numerical solution in the Lorenz system with long-time integration and a new multiple-precision-based approach used to identify the maximum effective computation time （MECT） and optimal step-size （OS）. In addition, the authors introduce how to analyze round-off error in a long-time integration in some typical cases of nonlinear systems and present its approximate estimate expression.
基金the National Key Research and Development Program of China(No.2017YFA0700900,2017YFA0700902,2017YFA0700901,2017YFB1003101)the National Natural Science Foundation of China(No.61472396,61432016,61473275,61522211,61532016,61521092,61502446,61672491,61602441,61602446,61732002,61702478,61732020)+4 种基金Beijing Natural Science Foundation(No.JQ18013)the National Basic Research Program of China(No.2015CB358800)National Science and Technology Major Project(No.2018ZX01031102)the Transformation and Transfer of Scientific and Technological Achievements of Chinese Academy of Sciences(No.KFJ-HGZX-013)Strategic Priority Research Program of Chinese Academy of Science(No.XDB32050200).
文摘Deep learning algorithms are the basis of many artificial intelligence applications.Those algorithms are both computationally intensive and memory intensive,making them difficult to deploy on embedded systems.Thus various deep learning accelerators(DLAs)are proposed and applied to achieve better performance and lower power consumption.However,most deep learning accelerators are unable to support multiple data formats.This research proposes the MW-DLA,a deep learning accelerator supporting dynamic configurable data-width.This work analyzes the data distribution of different data types in different layers and trains a typical network with per-layer representation.As a result,the proposed MW-DLA achieves 2X performance and more than 50%memory requirement for AlexNet with less than 5.77%area overhead.
