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arXiv 2021年

作者： Li, Kaiwen Zhang, Tao Wang, Rui Wang, Yuheng Han, Yi College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKL-MSI2T Changsha410073 China Graduate College National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha410073 China

This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the Multi-head Attention to capture the structural patterns, and design a dynamic embedding to handle the dynamic patterns of the problem. Once the model is trained, it can generalize to various types of CSP tasks (different sizes and topologies) with no need of re-training. Through controlled experiments, the proposed approach shows desirable time complexity: it runs more than 20 times faster than the traditional heuristic solvers with a tiny gap of optimality. Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large-scale and require quick decisions. Copyright © 2021, The Authors. All rights reserved.

关键词： Combinatorial optimization

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ME-TCAM: Memory-Efficient Ternary Content Addressable Memory Based on Multipumping-Enabled LUTRAM on FPGA

ME-TCAM: Memory-Efficient Ternary Content Addressable Memory...

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Intelligent Computing and Human-computer Interaction (ICHCI), International Conference on

作者： Zilin Shi Hui Yang Rulin Liu Jinli Yan Peng Qiao Baosheng Wang College of Computer Science and Technology National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China

Static random access memory (SRAM) on field programmable gate arrays (FPGAs) can be emulated to offer ternary content addressable memory (TCAM) functionality. However, SRAM-based TCAM wastes storage resources. This is due to the limited capacity of the physical addresses in the SRAM unit. This work proposes a LUTRAM-based TACM scheme on the FPGA called Memory-Efficient TCAM (ME-TCAM). METCAM divides SRAM unit into multiple virtual blocks mapping to a portion of the TCAM table to store the more address information of the TCAM table. Operation on SRAM block means that increasing the overall emulated TCAM bits/SRAM. Moreover, ME-TCAM exploits Xilinx primitives to conFigure lookup tables (LUTs) as 32 × 2 lookup table RAMs (LUTRAMs). We implement ME-TCAM using LUTRAM with a size of 512 × 48 and 1024 × 144 on a Virtex-7 FPGA device. Compared with the state-of-the-art research DUR, ME-TCAM achieves at least 2.6 times more memory efficiency.

关键词： Human computer interaction Associative memory Memory management Random access memory Logic gates Table lookup parallel architectures

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A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices

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Procedia computer Science 2020年 174卷 2-8页

作者： Zhiqiang Liu Jingfei Jiang Guoqing Lei Kai Chen Buyue Qin Xiaoqiang Zhao Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China

This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-accelerator that supports a variety of CNN models with a list of macro instructions. For demonstration, we implement a prototype on an FPGA platform with the RISC-V CPU working under 20 MHz and the CNN accelerator working under 100 MHz. As a case study, we run a CNN-based face detection and recognition application on this prototype. The prototype can process one image in 0.72 seconds and an ASIC implementation working under 400 MHz can process one image in less than 0.15 seconds by estimation, which can satisfy the needs for many IoT scenarios such as access control systems and check-in systems.

关键词： Processor RISC-V Accelerator AI (Artificial Intelligence) CNN (Convolutional Neural Network) IoT (Internet of Things)

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Optimizing One by One Direct Convolution on ARMv8 Multi-core CPUs

Optimizing One by One Direct Convolution on ARMv8 Multi-core...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Qinglin Wang Dongsheng Li Songzhu Mei Siqi Shen Xiandong Huang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead and calls matrix multiplication routines, which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.

关键词： Convolution Registers Layout Neural networks parallel processing computer architecture Tensile stress

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Efficient Management and Intelligent Fault Tolerance for HPC Interconnect Networks

Efficient Management and Intelligent Fault Tolerance for HPC...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Jijun Cao Mingche Lai Zhang Luo jiaqing Xu Zhengbin Pang College of Computer National Laboratory for Parallel and Distributed Processing ChangSha China

ISBN: (纸本)9781728125848

Interconnect Network is the key component in high performance computing system. With the incoming era of Exa-Scale (10 18 FLOPS) computing, designing large scale interconnect networks is facing with serious challenges in network management and fault tolerance. To construct higher performance and more reliable interconnect networks, we propose an efficient and intelligent network management architecture for indirect interconnect networks. This paper emphatically introduces the network management architecture, the in-band management channels in the Network Interface Chips (NIC) and Network Routing Chips (NRC) respectively, efficient centralized network management approach, distributed intelligent faulttolerant routing management, and so on. Based on the prototype system, the Control and Status Registers (CSR) accessing latency performance of in-band network management is evaluated. Also based on a customized simulation model for indirect interconnect networks, the typical distributed fault-tolerant scenes are tested. The experiment results show the in-band network management can averagely achieves 716 times improvements than the out of-band network management. Moreover, by running heuristic algorithms to automatically reconstruct routing tables for failure links or routers, the intellectual network management Engine can achieve fault-tolerant routing to maximizing network performance.

关键词： High performance computing system Interconnect network Network management Fault tolerate Network management Fault tolerance High Performance Computing unified network management architecture Network Network routing

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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IEEE International Conference on Artificial Intelligence and computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence distributed processing Software engineering

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer Topology 33rd

CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer T...

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33rd International Conference on Advanced Information Networking and Applications, AINA 2019

作者： Zheng, Li-ming Long, Wen-feng Liu, Yu-Jia Sun, Wei-dong Department of Message Communication Armed Police Officer Academy Chengdu China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030150341

Data distribution is a key technology for resources convergence and sharing in distributed environment. To better meet the requirement for real time data distribution in the dynamic network, a trace routing algorithm named CRAWL based on the hybrid two-layered topology is put forward. The algorithm contains an overlay topology named CBDLO, upper of which consists of multiple distributed balanced binary trees corresponding to different properties and the lower of which is an unstructured topology. CRAWL forwards the data on the lower unstructured topology in the form of random walk, so that the data can be sent to the corresponding upper topology entry. It also includes a matching algorithm named CDM for the parallel matching data properties on the upper distributed and balanced binary tree and transmitting the matched data to the nodes that are interested in the data. The experimental results show that the algorithm can effectively support large-scale data distribution in a dynamical network, reduce distribution overhead and matching delays. © 2019, Springer Nature Switzerland AG.

关键词： Routing algorithms

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Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data 2nd

Secure and Fast Decision Tree Evaluation on Outsourced Cloud...

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2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019

作者： Liu, Lin Su, Jinshu Chen, Rongmao Chen, Jinrong Sun, Guangliang Li, Jie School of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9783030306182

Decision trees are famous machine learning classifiers which have been widely used in many areas, such as healthcare, text classification and remote diagnostics, etc. The service providers usually host a decision tree model on the cloud server and provide some classification service for clients to use such a model remotely. In such a scenario, the model is a valuable asset to the cloud which should not be disclosed to the clients, while the query data and classification results are private to the client. To solve such a problem, we propose several building blocks, i.e., secure comparison and secure polynomial calculation, in a two-cloud model. Based on these building blocks, we design a privacy-preserving decision tree evaluation scheme. Compared with the most recent works, our scheme can fully protect the tree model and clients’ data privacy simultaneously. Besides, our scheme also supports offline service users which is essential to the system’s scalability. Moreover, through theoretical analysis and real-world experimental test, it is oblivious that our scheme is quite efficient. © 2019, Springer Nature Switzerland AG.

关键词： Decision trees

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parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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