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A Dynamic Mapping Model for General CNN Accelerator Based on FPGA 17th

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A Dynamic Mapping Model for General CNN Accelerator Based on...

17th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2020

作者： Zhao, Xiaoqiang Jiang, Jingfei Han, Zhe Xu, Jinwei Liu, Zhiqiang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China

ISBN: (纸本)9783030794774

As the application scenarios of convolutional neural network (CNN) become more and more complex, the general CNN accelerator based on matrix multiplication has become a new research focus. The existing mapping methods for converting convolution calculation into matrix multiplication need to be improved. This paper proposes a new dynamic mapping model to improve the flexibility and versatility of matrix multiplication. The dynamic mapping model implements two algorithms: dynamic residue processing mapping algorithm (DRPMA) and dilated convolution mapping algorithm (DCMA). The former can dynamically adjust the mapping method according to the number of output channels of the convolution layer, improve the utilization of the multiply-accumulate (MAC) array. The latter extends the efficient support for Dilated CNNs. For demonstration, we implement an accelerator with Verilog on Xilinx VC709 FPGA board and test some typical CNN models. Experimental results show that the general accelerator achieves high performance and energy efficiency. © 2021, IFIP International Federation for Information processing.

关键词： Field programmable gate arrays (FPGA)

AUXILIARY-TASKS LEARNING FOR PHYSICS-INFORMED NEURAL NETWORK-BASED PARTIAL DIFFERENTIAL EQUATIONS SOLVING

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arXiv 2023年

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhou, Enqiang Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China

Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks. However, original PINNs often suffer from bottlenecks, such as low accuracy and non-convergence, limiting their applicability in complex physical contexts. To alleviate these issues, we proposed auxiliary-task learning-based physics-informed neural networks (ATL-PINNs), which provide four different auxiliary-task learning modes and investigate their performance compared with original PINNs. We also employ the gradient cosine similarity algorithm to integrate auxiliary problem loss with the primary problem loss in ATL-PINNs, which aims to enhance the effectiveness of the auxiliary-task learning modes. To the best of our knowledge, this is the first study to introduce auxiliary-task learning modes in the context of physics-informed learning. We conduct experiments on three PDE problems across different fields and scenarios. Our findings demonstrate that the proposed auxiliary-task learning modes can significantly improve solution accuracy, achieving a maximum performance boost of 96.62% (averaging 28.23%) compared to the original single-task PINNs. The code and dataset are open source at https://***/junjun-yan/ATL-PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

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arXiv 2023年

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhoui, Enqiang Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x. The code of ST-PINN is available at GitHub: https://***/junjun-yan/ST-PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

Exploring Policy Diversity in parallel Actor-Critic Learning

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Exploring Policy Diversity in Parallel Actor-Critic Learning

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yanqiang Zhang Yuanzhao Zhai Gongqian Zhou Bo Ding Dawei Feng Songwang Liu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Academy of Military Science Beijing China

Exploration is a critical challenge for deep reinforcement learning methods. Although existing works such as actor-critic algorithms have made much progress, most still suffer from the sample inefficiency problem in complex environments where rewards are sparse. parallel sampling, which uses multiple actors with the same policy interacting with the environment, is an effective approach to improve sample efficiency. However, parallel parameter-sharing actors collect similar samples, which generally hinders the improvement of the overall exploration process. In this paper, we propose a Policy Diversity enhanced approach for parallel Actor-Critic (PDAC). Specifically, we extend the parallel actor-critic architecture to the PDAC framework composed of a shared critic and parallel distinct actors. Then we introduce the KL-divergence of the action probability distribution between parallel actors as the intrinsic reward to encourage actors to explore diverse strategies. We evaluate our approach in multiple challenging procedurally-generated tasks and compare it with state-of-the-art algorithms. Experiments show that PDAC makes significant progress in the comparison, in terms of cumulative rewards and sample efficiency.

关键词： Deep learning Reinforcement learning Learning (artificial intelligence) Probability distribution Task analysis

F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud

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F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jiacheng Yang Guodong Yi Fei Gao Peichang Shi Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： Industries Data centers Cloud computing Job shop scheduling Costs Scheduling algorithms Large language models

DCSA: The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment

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DCSA: The Deployment Mechanism of Chained Serverless Applica...

IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

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arXiv 2024年

作者： Wang, Zhichao C. Chen, Xinhai C. Gong, Chunye Y. Yang, Bo Deng, Liang Sun, Yufei F. Pang, Yufei F. Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment China China Aerodynamics Research and Development Center China Nankai University College of Software China

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models’ learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh. © 2024, CC BY.

关键词： Deep reinforcement learning

Proposing an intelligent mesh smoothing method with graph neural networks

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arXiv 2023年

作者： Wang, Zhichao Chen, Xinhai Yan, Junjun Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology National University of Defense Technology Changsha410073 China

In CFD, mesh smoothing methods are commonly utilized to refine the mesh quality to achieve high-precision numerical simulations. Specifically, optimization-based smoothing is used for high-quality mesh smoothing, but it incurs significant computational overhead. Pioneer works improve its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes. However, they pose difficulty in smoothing the mesh nodes with varying degrees and also need data augmentation to address the node input sequence problem. Additionally, the required labeled high-quality meshes further limit the applicability of the proposed method. In this paper, we present GMSNet, a lightweight neural network model for intelligent mesh smoothing. GMSNet adopts graph neural networks to extract features of the node's neighbors and output the optimal node position. During smoothing, we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements. With a lightweight model, GMSNet can effectively smoothing mesh nodes with varying degrees and remain unaffected by the order of input data. A novel loss function, MetricLoss, is also developed to eliminate the need for high-quality meshes, which provides a stable and rapid convergence during training. We compare GMSNet with commonly used mesh smoothing methods on two-dimensional triangle meshes. The experimental results show that GMSNet achieves outstanding mesh smoothing performances with 5% model parameters of the previous model, and attains 13.56 times faster than optimization-based smoothing. © 2023, CC BY.

关键词： Graph neural networks

Two-Stage Convolutional Neural Network for Knee Osteoarthritis Diagnosis in X-Rays

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Two-Stage Convolutional Neural Network for Knee Osteoarthrit...

International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021

作者： Wang, Kang Niu, Xin Dou, Yong Yang, Di Xie, Dongxing Yang, Tuo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China Department of Orthopaedics Xiangya Hospital Central South University Changsha410008 China Department of Health Management Center Xiangya Hospital Central South University Changsha410008 China

ISBN: (纸本)9789811638794

Knee osteoarthritis (OA) is a common musculoskeletal illness. To solve the problem that inaccurate knee joint localization and inadequate knee OA features extracted from plain radiographs affect the accuracy of knee OA diagnosis in X-rays, we propose a novel Two-Stage Convolutional Neural Network (TS-CNN) method, consisting of the KneeDetnet and the KLnet. The KneeDetnet with two small multi-task convolutional neural networks is proposed to locate knee joints, improving the accuracy of knee joint localization. Then KLnet is designed to assess knee OA, where a shared Siamese network via ResNet is used to extract more discriminative deep learning features that are fused with gender information for obtaining richer features. Our method is evaluated on public OAI and MOST datasets. The highest detection accuracy of knee joints can reach 99.93% and 99.02% on two datasets, respectively. The KLnet algorithm achieves 78.85% and 68.20% prediction accuracy on the OAI and MOST datasets, respectively. Experimental results show that our method outperforms the existing workhorse. The proposed approach may become a potentially useful tool for assisting physicians. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks