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检索条件"任意字段=2016 International Congress on Computation Algorithms in Engineering"

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Scalable and modularized RTL compilation of Convolutional Neural Networks onto FPGA

Scalable and modularized RTL compilation of Convolutional Ne...

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international Conference on Field Programmable Logic and Applications

作者： Yufei Ma Naveen Suda Yu Cao Jae-sun Seo Sarma Vrudhula Computer and Energy Engineering Arizona State University Tempe USA Decision Systems Engineering Arizona State University Tempe USA

Despite its popularity, deploying Convolutional Neural Networks (CNNs) on a portable system is still challenging due to large data volume, intensive computation and frequent memory access. Although previous FPGA acceleration schemes generated by high-level synthesis tools (i.e., HLS, OpenCL) have allowed for fast design optimization, hardware inefficiency still exists when allocating FPGA resources to maximize parallelism and throughput. A direct hardware-level design (i.e., RTL) can improve the efficiency and achieve greater acceleration. However, this requires an in-depth understanding of both the algorithm structure and the FPGA system architecture. In this work, we present a scalable solution that integrates the flexibility of high-level synthesis and the finer level optimization of an RTL implementation. The cornerstone is a compiler that analyzes the CNN structure and parameters, and automatically generates a set of modular and scalable computing primitives that can accelerate various deep learning algorithms. Integrating these modules together for end-to-end CNN implementations, this work quantitatively analyzes the complier's design strategy to optimize the throughput of a given CNN model with the FPGA resource constraints. The proposed methodology is demonstrated on Altera Stratix-V GXA7 FPGA for AlexNet and NIN CNN models, achieving 114.5 GOPS and 117.3 GOPS, respectively. This represents a 1.9× improvement in throughput when compared to the OpenCL-based design. The results illustrate the promise of the automatic compiler solution for modularized and scalable hardware acceleration of deep learning.

关键词： Field programmable gate arrays Convolution Hardware Kernel Acceleration Memory management Algorithm design and analysis

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A Unified Resource-Constrained Framework for Graph SLAM

A Unified Resource-Constrained Framework for Graph SLAM

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IEEE international Conference on Robotics and Automation

作者： Liam Paull Guoquan Huang John J. Leonard the Computer Science and Artificial Intelligence Laboratory (CSAIL) MIT Cambridge MA 02139 USA the Dept. of Mechanical Engineering University of Delaware Newark DE 19716 USA

ISBN: (纸本)9781467380270

Graphical methods have proven an extremely useful tool employed by the mobile robotics community to frame estimation problems. Incremental solvers are able to process incoming sensor data and produce maximum a posteriori (MAP) estimates in realtime by exploiting the natural sparsity within the graph for reasonable-sized problems. However, to enable truly longterm operation in prior unknown environments requires algorithms whose computation, memory, and bandwidth (in the case of distributed systems) requirements scale constantly with time and environment size. Some recent approaches have addressed this problem through a two-step process - first the variables selected for removal are marginalized which induces density, and then the result is sparsified to maintain computational efficiency. Previous literature generally addresses only one of these two components. In this work, we attempt to explicitly connect all of the aforementioned resource constraint requirements by considering the node removal and sparsification pipeline in its entirety. We formulate the node selection problem as a minimization problem over the penalty to be paid in the resulting sparsification. As a result, we produce node subset selection strategies that are optimal in terms of minimizing the impact, in terms of Kullback-Liebler divergence (KLD), of approximating the dense distribution by a sparse one. We then show that one instantiation of this problem yields a computationally tractable formulation. Finally, we evaluate the method on standard datasets and show that the KLD is minimized as compared to other commonly-used heuristic node selection techniques.

关键词： KLD minimized compared

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A novel evolutionary algorithm for the homogeneous Probabilistic Traveling Salesman Problem

A novel evolutionary algorithm for the homogeneous Probabili...

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international Conference on Computer and Information Science (ACIS)

作者： Michael Manuel Smith Yun Shiow Chen Department of Industrial Engineering and Management Yuan Ze University Chung-Li Tao Yuan Taiwan R.O.C.

ISBN: (纸本)9781509008070

Amid the most widely studied NP-hard combinatorial optimization problems, the Probabilistic Traveling Salesman Problem (PTSP), which is an extension of the well-known Traveling Salesman Problem, offers a fundamental basis for analyzing the stochastic impacts in routing problems. In this paper, a new meta-heuristic approach, Genetic Minimum Matrix Search (GMMS), is introduced for the solution of the Probabilistic Traveling Salesman Problem. This evolutionary algorithm is founded upon the Minimum Matrix Search construction algorithm that uses dimension reduction in the matrix edge set to obtain a reduce matrix network within which lies the optimal solution in whole or clusters. GMMS uses family competitive metamorphosis and short-term memory selection to maintain the diversity of the population in each generation which is useful in avoiding local optimum. The proposed evolutionary algorithm showed very satisfactory results in testing on well-known benchmark problems from TSPLIB. Comparisons is performed with the results of numerous implementations of well-known meta-heuristic approaches from the literature, such as; Honey Bee Mating Optimization, Particle Swarm Optimization, Tabu Search, Greedy Randomized Adaptive Search Procedure, Genetic Algorithm, Ant Colony Optimization, etc. Having found 9 new best solution out of 20 cases, the computational study shows that the GMMS algorithm is a competitive tool for solving the PTSP.

关键词： Sociology Statistics Urban areas Heuristic algorithms Traveling salesman problems Evolutionary computation Probabilistic logic

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Fault section locating for distribution network with DG based on improved ant colony algorithm

Fault section locating for distribution network with DG base...

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international Conference on Power Electronics and Motion Control (IPEMC)

作者： Weiqing Tao Gang Yang Junyu Zhang School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (纸本)9781509012114

As distributed generators in distribution networks have brought much influence to the fault current, the traditional fault-section locating algorithm of distribution networks may not work. In this paper, a new switching function is built which can be used in the distribution network with single power supply and multi power supplies. A regional processing method is used in this paper, which divides the distribution network into several independent regions, then the fault locating algorithm is used in these independent regions. The regions without DG connection are eliminated if fault current is not detected. So the complexity of computation of fault-section location is reduced. Ant colony algorithm is a good optimization algorithm of swarm intelligence. Because of its positive feedback, fault tolerance and distributed computing features, good application effect of locating faulty section in distribution network can be attained. In order to overcome the disadvantages such as large amounts of calculation, long searching time, local optimal. Ant colony algorithm is improved by adaptive dynamic modifying pheromone. Ant system with elitist strategy and MMAS are also used in this paper. This improved algorithm is used in distribution network with multi distributed generators. The results of example analysis show the effective and good fault tolerance of the algorithm.

关键词： Switches Heuristic algorithms Circuit faults Fault currents Power supplies Algorithm design and analysis Power electronics

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Towards a smart selection of resources for low-energy multimedia processing

Towards a smart selection of resources for low-energy multim...

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international Conference on Cloud Technologies and Applications (CloudTech)

作者： Sidi Ahmed Mahmoudi Faculty of Engineering University of Mons Belgium

ISBN: (纸本)9781467388955

Today, one can find images and videos everywhere, they can come from cameras, mobile phones or from other devices. These images and videos are used to illustrate different objects in a large number of situations (airports, hospitals, public areas, sport events, etc.). This makes the task of processing images and videos a necessary tool that can be used for various domains related to computer vision. The performance of these algorithms have been so reduced due to their high intensive computation and energy consumption. In this work, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in case of processing single image, multiple images, multiple videos or single video in real time. This framework enables to affect the CPU or/and GPU units for calculation depending on the type of media to process and the algorithm complexity. The framework provides several image and video functions on GPU, such as silhouette extraction, points of interest extraction, edges detection, sparse and dense optical flow estimation. These functions are exploited in different applications such as vertebra segmentation in X-ray and MR images, gaze estimation, event detection and localization in real time. Experimental results have been obtained by applying the framework for different use case applications showing a speedup ranging from 5 to 116×, by comparison with sequential CPU implementations. In addition to these performance, the parallel and heterogeneous implementation offered lower power consumption as a result for the fast treatment.

关键词： Biomedical imaging Performance evaluation Estimation Cameras Image edge detection Hardware Graphics processing units

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A energy efficient multi-dimension model for system control in smart environment systems

A energy efficient multi-dimension model for system control ...

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IEEE international Conference on Industrial Informatics (INDIN)

作者： Anlong Ming Hong Luo Yanchen Ren Zhibo Pang Kim-Fung Tsang Dep. of Computer Sci. / Inst. of Sensing Tech. & Business Beijing Univ. of Posts and Telecomm Beijing China ABB Corporate Research Västerås Sweden Department of Electronics Engineering City University of Hong Kong Hong Kong China

ISBN: (纸本)9781509028719

A smart environment system should automatically control the devices according to the sensing information and users' requirements so as to keep the environmental elements (e.g., temperature, light) within the desired range. System control with minimum power is one key issue in such a system. In this paper, we propose a multi-dimension model for system control. In this model, each environmental element is abstracted into a dimension, such that a service with conditions and targets can be formulated as a multi-dimensional service space, and a smart environment with many services may map to a comprehensive multi-dimensional service space through space computation. Based on this model, we propose a minimum power adjustment algorithm for energy-efficient scheduling in smart environment, which transforms the optimal control problem into the problem of the shortest weighted distance of point-to-polygonal in multi-dimensional space. Theoretical analysis and experimental results show that the proposed model is effective and efficient in energy-efficient system control. It is important to point out that the proposed algorithms are scalable when the number of dimensions or services increases.

关键词： Aerospace electronics Sensors computational modeling Energy efficiency Optimal control Actuators

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Crime Mapping and Predictive Analysis of Crimes in Maryland, USA

Crime Mapping and Predictive Analysis of Crimes in Maryland,...

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Signal Processing, computation, Electronics, Power and Telecommunication (IConSCEPT), international Conference on

作者： Beryl Abey Thomas Sivakami Raja School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

The issue of crime has long existed, both nationally and internationally. Several research have looked at previous crimes and provided appropriate preventive measures. However, even in recent years, there has been a high prevalence of crime. Therefore, the primary objective of this study is to analyze the crimes that took place in Maryland, USA, between 2016 and 2020. After pre-processing and cleaning the dataset, numerous analysis and visualization findings were drawn. Improving public safety and crime prevention in the state of Maryland requires first developing a predictive analytic and crime mapping technology. By examining past trends and data, it was possible to ascertain the days of the week, time of the day, and months of the year that crimes were most likely to occur. In addition, the location of these occurrences and the most frequent form of crime each year could be inferred. The three primary machine learning algorithms that were employed to predict the kind of crime that would occur given other factors were K Neighbors Classifier, Random Forest Classifier, and Logistic Regression. The accuracy was moderate to low. The Random Forest Classifier, Logistic Regression, and K Neighbors Classifier displayed an accuracy of 52.55%, 43.86% and 38.39%. Nevertheless, by displaying past crime data trends, law enforcement officials can identify crime hotspots, identify emerging trends, and allocate resources efficiently. This would enable law enforcements to anticipate and prevent crimes before they occur, allowing for proactive interventions.

关键词： Logistic regression Accuracy Law enforcement Prevention and mitigation Predictive models Signal processing Market research

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Modeling, programming and performance analysis of automotive environment map representations on embedded GPUs

Modeling, programming and performance analysis of automotive...

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IEEE international High-Level Design Validation and Test Workshop

作者： Jorg Fickenscher Oliver Reiche Jens Schlumberger Frank Hannig Jurgen Teich Hardware/Software Co-Design University Erlangen-Nürnberg (FAU) Germany

Future Advanced Driver Assistance Systems (ADAS) require the continuous computation of detailed maps of the vehicle's environment. Due to the high demand of accuracy and the enormous amount of data to be fused and processed, common architectures used today, like single-core processors in automotive Electronic Control Units (ECUs), do not provide enough computing power. Here, emerging embedded multi-core architectures are appealing such as embedded Graphics Processing Units (GPUs). In this paper, we (a) identify and analyze common subalgorithms of ADAS algorithms for computing environment maps, such as interval maps, for suitability to be parallelized and run on embedded GPUs. From this analysis, (b) performance models are derived on achievable speedups with respect to sequential single-core CPU implementations. (c) As a third contribution of this paper, these performance models are validated by presenting and comparing a novel parallelized interval map GPU implementation against a parallel occupancy grid map implementation. For both types of environment maps, implementations on an Nvidia Tegra K1 prototype are compared to verify the correctness of the introduced performance models. Finally, the achievable speedups with respect to a single-core CPU solution are reported. These range from 3x to 275x for interval and grid map computations.

关键词： Graphics processing units Sensors Vehicles computational modeling Algorithm design and analysis Two dimensional displays Automotive engineering

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Comparative performance of QPSO and BSA for design of linear array of parallel dipole antennas with mutual coupling

Comparative performance of QPSO and BSA for design of linear...

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international Conference on Microelectronics, Computing and Communications (MicroCom)

作者： Hemant Patidar Ananya Mahanti Rohit Sharma G. K. Mahanti Department of Electronics and Communication Engineering National Institute of Technology Durgapur India

ISBN: (纸本)9781467366229

The authors presented two different optimization methods-one is quantum particle swarm optimization (QPSO) and another is backtracking search algorithm (BSA). Combined with inverse fast fourier transform (IFFT), the paper deals with the synthesis of array of linearly spaced and mutually coupled half wavelength very thin vertical and parallel dipoles with fixed value of voltage standing wave ratio (VSWR) and side lobe level. The performances of the two algorithms are compared in terms of different antenna parameters like side lobe level, VSWR as well as statistical parameters such as best fitness value, mean, standard deviation and computation time. There exists mutual coupling among the parallel half-wavelength dipole antennas. Self and mutual impedances are derived from induced EMF method assuming distribution of current on each dipole is sinusoidal. The pattern so generated is broadside in the horizontal plane. There exists a relationship between the IFFT and the array factor for an array of linearly and uniformly spaced isotropic antennas. Array factor is found out by applying inverse fast fourier transform on the element excitations that in turn reduces the computational time. It is assumed that the element pattern of vertical half-wavelength dipole antenna is omni-directional in the horizontal plane. One example is presented to prove the effective use of the approach proposed.

关键词： Dipole antennas Mutual coupling Linear antenna arrays Optimization Phased arrays Algorithm design and analysis

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HPSVM: Heterogeneous Parallel SVM with Factorization Based IPM Algorithm on CPU-GPU Cluster

HPSVM: Heterogeneous Parallel SVM with Factorization Based I...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Tao Li Xuechen Liu Qiankun Dong Wenjing Ma Kai Wang College of Computer and Control Engineering Nankai University Tianjin China Institute of computing Chinese Academy of Sciences Beijing China

Support vector machine (SVM) is a supervised method widely used in the statistical classification and regression analysis. SVM training can be solved via the interior point method (IPM) with the advantages of low storage, fast convergence and easy parallelization. However, it is still confronted with the challenges of training speed and memory use. In this paper, we propose a parallel primal-dual IPM algorithm based on the incomplete Cholesky factorization (ICF) for efficiently training large-scale SVMs, named HPSVM, on CPU-GPU cluster. Our approach is distinguished from earlier work in that it is specifically designed to take maximal advantage of the CPU-GPU collaborative computation with the dual buffers 3-stage pipeline mechanism, and efficiently handles large-scale training datasets. In HPSVM, the heterogeneous hierarchical memory is fully explored to alleviate the bottleneck for optimizing data transfer, and the programming paradigm is presented to build an efficient collaboration mechanism between CPU and GPU. Comprehensive experiments show that HPSVM is up to 11 times faster than the CPU version on real datasets.

关键词： Support vector machines Training Graphics processing units Clustering algorithms Algorithm design and analysis computational efficiency Acceleration

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