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EdgeNN: Efficient Neural Network Inference for CPU-GPU Integrated Edge Devices

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EdgeNN: Efficient Neural Network Inference for CPU-GPU Integ...

International Conference on Data engineering

作者： Chenyang Zhang Feng Zhang Kuangyu Chen Mingjun Chen Bingsheng He Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China School of Computing National University of Singapore

With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. This paper extensively explores neural network inference on integrated edge devices and proposes EdgeNN, the first neural network inference solution on CPU-GPU integrated edge devices. EdgeNN has three novel characteristics. First, EdgeNN can adaptively utilize the unified physical memory and conduct the zero-copy optimization. Second, EdgeNN involves a novel inference-targeted inter- and intra-kernel CPU-GPU hybrid execution approach, which co-runs the CPU with the GPU to fully utilize the edge device’s computing resources. Third, EdgeNN adopts a fine-grained adaptive inference tuning approach, which can divide the complicated inference structure into sub-tasks mapped to the CPU and the GPU. Experiments show that on six popular neural network inference tasks, EdgeNN brings an average of 3.97×, 3.12×, and 8.80× speedups to inference on the CPU of the integrated device, inference on a mobile phone CPU, and inference on an edge CPU device. Additionally, it achieves 22.02% time benefits to the direct execution of the original programs. Specifically, 9.93% comes from better utilization of unified memory, and 10.76% comes from CPU-GPU hybrid execution. Besides, EdgeNN can deliver 29.14× and 5.70× higher energy efficiency than the edge CPU and the discrete GPU, respectively. We have made EdgeNN available at https://***/ChenyangZhang-cs/EdgeNN.

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OPTIMIZATION ON PRODUCT MANIFOLDS UNDER A PRECONDITIONED METRIC

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arXiv 2023年

作者： Gao, Bin Peng, Renfeng Yuan, Ya-Xiang State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China

Since optimization on Riemannian manifolds relies on the chosen metric, it is appealing to know that how the performance of a Riemannian optimization method varies with different metrics and how to exquisitely construct a metric such that a method can be accelerated. To this end, we propose a general framework for optimization problems on product manifolds endowed with a preconditioned metric, and we develop Riemannian methods under this metric. Generally, the metric is constructed by an operator that aims to approximate the diagonal blocks of the Riemannian Hessian of the cost function. We propose three specific approaches to design the operator: exact block diagonal preconditioning, left and right preconditioning, and Gauss–Newton type preconditioning. Specifically, we tailor new preconditioned metrics and adapt the proposed Riemannian methods to the canonical correlation analysis and the truncated singular value decomposition problems, which provably accelerate the Riemannian methods. Additionally, we adopt the Gauss–Newton type preconditioning to solve the tensor ring completion problem. Numerical results among these applications verify that a delicate metric does accelerate the Riemannian optimization *** Codes 15A69, 65K05, 65F30, 90C30 Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

GLOBAL STRONG SOLUTION FOR THE STOCHASTIC TAMED CHEMOTAXIS-NAVIER-STOKES SYSTEM IN R3

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arXiv 2024年

作者： Xu, Fan Zhang, Lei Liu, Bin School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China

In this work, we consider the 3D Cauchy problem for a coupled system arising from the biomathematics, which consists of a chemotaxis model with cubic logistic source and the stochastic tamed Navier-Stokes equations (STCNS, for short). Our main goal is to establish the existence and uniqueness of global strong solution (strong in both the probabilistic and PDE senses) for the 3D STCNS system with large initial data. To achieve this, we first introduce a triple approximation scheme by virtue of the Friedrichs mollifier, frequency truncation operators and cut-off functions, which makes it possible for constructing sufficiently smooth approximation solutions, and facilitating the effective use of stochastic entropy-energy estimates. Then, building on the entropy-energy estimates, we further derive some higher-order energy estimates, which together with the stochastic compactness method permits us to construct the strong solution for the STCNS system. Copyright © 2024, The Authors. All rights reserved.

关键词： Navier Stokes equations

WELL-POSEDNESS AND LARGE DEVIATIONS OF LÉVY-DRIVEN MARCUS STOCHASTIC LANDAU-LIFSHITZ-BARYAKHTAR EQUATION

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arXiv 2024年

作者： Xu, Fan Liu, Bin Zhang, Lei School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China

This paper considers the stochastic Landau-Lifshitz-Baryakhtar (SLLBar) equation with pure jump noise in Marcus canonical form, which describes the dynamics of magnetic spin field in a ferromagnet at elevated temperatures with the effective field Heff influenced by external random noise. Under the natural assumption that the magnetic body O ⊂ Rd (d = 1, 2, 3) is bounded with smooth boundary, we shall prove that the initial-boundary value problem of SLLBar equation possesses a unique global probabilistically strong and analytically weak solution with initial data in the energy space H1(O). Then by employing the weak convergence method, we proceed to establish a Freidlin-Wentzell type large deviation principle for pathwise solutions to the SLLBar equation. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

NEW GLOBAL CARLEMAN ESTIMATES AND NULL CONTROLLABILITY FOR FORWARD/BACKWARD SEMI-LINEAR PARABOLIC SPDES

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arXiv 2024年

作者： Zhang, Lei Xu, Fan Liu, Bin School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China

In this paper, we study the null controllability for some linear and semi-linear parabolic SPDEs involving both the state and the gradient of the state. To start with, an improved global Carleman estimate for linear forward (resp. backward) parabolic SPDEs with general random coefficients and L2-valued source terms is derived. Based on this, we further develop a new global Carleman estimate for linear forward (resp. backward) parabolic SPDEs with H-1-valued source terms, which enables us to deal with the global null controllability for linear backward (resp. forward) parabolic SPDEs with gradient terms. As byproduct, a special energy-type estimate for the controlled system that explicitly depends on the parameters λ, μ and the weighted function θ is obtained. Furthermore, by employing a fixed-point argument, we extend the previous linear controllability results to some semi-linear backward (resp. forward) parabolic SPDEs. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllability

Preparing quantum states by measurement-feedback control with Bayesian optimization

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Frontiers of physics 2023年第6期18卷 291-299页

作者： Yadong Wu Juan Yao Pengfei Zhang Department of Physics Fudan UniversityShanghai 200438China State Key Laboratory of Surface Physics Key Laboratory of Micro and Nano Photonic Structures(MOE)Fudan UniversityShanghai 200438China Institute for Nanoelectronic Devices and Quantum Computing Fudan UniversityShanghai 200433China Shanghai Qi Zhi Institute AI TowerXuhui DistrictShanghai 200232China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China International Quantum Academy Shenzhen 518048China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Walter Burke Institute for Theoretical Physics&Institute for Quantum Information and Matter California Institute of TechnologyPasadenaCA 91125USA

The preparation of quantum states is crucial for enabling quantum computations and *** this work,we present a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process(MFCP)with machine learning ***,we employ Bayesian optimization(BO)to enhance the efficiency of determining the measurement and feedback operators within the *** an illustration,we study the ground state preparation of the one-dimensional Bose−Hubbard *** BO,we are able to identify optimal parameters that can effectively drive the system towards low-energy states with a high probability across various quantum *** results open up new directions for further exploration and development of advanced control strategies for quantum computations and simulations.

关键词： state preparation feedback control Bayesian optimization

BOUNDARY VALUE METHODS FOR CA PUTO FR ACTIONAL DIFFERENTIAL EQUATIONS

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Journal of Computational Mathematics 2021年第1期39卷 108-129页

作者： Yongtao Zhou Chengiian Zhang Huiru Wang School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

This paper deals with the numerical computation and analysis for Caputo fractional differential equations(CFDEs).By combining the p-order boundary value methods(B-VMs)and the m-th Lagrange interpolation,a type of exte... 详细信息

关键词： Fractional differential equations Caputo derivatives Boundary value methods Local stability Unique solvability Convergence.

GLOBAL WELL-POSEDNESS FOR THE LANDAU-LIFSHITZ-BARYAKHTAR EQUATION IN 3

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arXiv 2024年

作者： Xu, Fan Liu, Bin School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China

This paper establishes the global well-posedness of the Landau-Lifshitz-Baryakhtar (LLBar) equation in the whole space 3. The study first demonstrates the existence and uniqueness of global strong solutions using the weak compactness approach. Furthermore, the existence and uniqueness of classical solutions, as well as arbitrary smooth solutions, are derived through a bootstrap argument. The proofs for the existence of these three types of global solutions are based on Friedrichs mollifier approximation and energy estimates, with the structure of the LLBar equation playing a crucial role in the derivation of the results. Copyright © 2024, The Authors. All rights reserved.

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A CHEMOTAXIS-FLUID MODEL DRIVEN BY LÉVY NOISE IN R2

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arXiv 2024年

In this paper, we investigate the existence and uniqueness of global solutions to the Cauchy problem for a coupled stochastic chemotaxis-Navier-Stokes system with multiplicative Lévy noises in R2. The existence of global martingale solutions is proved under a framework that is based on the Faedo-Galerkin approximation scheme and stochastic compactness method, where the verification of tightness depends crucially on a novel stochastic version of Lyapunov functional inequality and proper compactness criteria in Fréchet spaces. A pathwise uniqueness result is also established with suitable assumption on the jump noises, which indicates that the considered system admits a unique global strong solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions