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The artificial intelligence inference model is currently the best mathematical model based on artificial neural networks. The intelligent reasoning mode based on artificial intelligence has a large construction scale. In a completed inference, many multiplication and addition operations need to be completed. To establish an effective artificial intelligence inference model, its computational complexity is dozens or even more times higher than traditional artificial intelligence algorithms. This article focused on the research on the isomerization acceleration of FPGA (Field Programmable Gate Array). This article was looking for a method that reduces both system changes and migration, while maximizing the flexibility of FPGA programmability. In order to build on this foundation, a fast interface for fast computation using FPGA was designed. The characteristic of this project was that it could combine the FPGA acceleration platform with ordinary computers or servers, and could effectively utilize traditional computer software and toolchains. This article compared the computational time consumption between the inference model and the CPU. In the scale calculation logic scenario of the inference model designed in this article, it could achieve the same computing power as the CPU when processing 70000 multiplication calculations. The experimental results indicated that the increase in CPU computing power was greater than that of the inference model. This indicated that the larger the amount of computational data, the more significant the acceleration effect of the inference model in this article would be. [ABSTRACT FROM AUTHOR]

The proposed work develops a rapid and automatic method for brain tumour detection and segmentation using multi-sequence magnetic resonance imaging (MRI) datasets available from BraTS. The proposed method consists of three phases: tumourous slice detection, tumour extraction and tumour substructures segmentation. In phase 1, feature blocks and SVM classifier are used to classify the MRI slices into normal or tumourous. Phase 2 contains fuzzy c means (FCM) algorithm to extract the tumour region from slices identified by phase 1. In addition, graphics processing unit (GPU) based FCM method has been implemented for reducing the processing time which is major overhead with FCM processing of MRI volumes. For phase 3, a novel probabilistic local ternary patterns (PLTP) technique is used to segment the tumour substructures based on the probability density value of histogram bins. Quantitative measures such as sensitivity, specificity, accuracy and dice values are used to analyses the performance of the proposed method and compare with state-of-art-methods. As post processing, the tumour volume estimation and 3D visualization were done for analyzing the nature and location of the tumour to the medical experts. Further, the availability of the GPU reduces the processing time up to 18× than serial CPU processing. [ABSTRACT FROM AUTHOR]

Abstract Spatter formation is a longstanding issue in laser welding. This research addresses this topic and proposes a machine vision algorithm executed on a graphics processing unit to detect spatters in real time. Using this approach, a control system detecting spatter at a rate of 1 kHz and with a resolution of 900 x 900 pixels was implemented. Based on an experimental series, it is shown that the variation of the process parameters has a significant influence on the formation of spatter. It was also possible to quantify the variance of the spatter formation for a given set of process parameters. [ABSTRACT FROM AUTHOR]

The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. [ABSTRACT FROM AUTHOR]

Transparent Optical Networks has been known as a potential solution for high speed flexible optical backbone networks since a long time ago. However, many scientific challenges remain to be overcome such as the problem of Optical Burst Grooming (OBG) with several conflicting objectives and constraints. OBG is a fundamental problem in the engineering, control, and management of optical traffic networks, and arises in most network design applications, including optical burst routing, survivability design, and traffic scheduling. The traffic grooming problem is to coalesce several high and low speed sub bursts close in time together to form a larger burst that will be switched as one unit. In this paper, we first formulate OBG as a Multi Objective Integer Linear Programming (MO-ILP) optimization problem. Then we propose to use a parallel and hierarchical solver based on Artificial Neural Networks (ANN) with Graphics Processing Unit (GPU) parallel implementation using Compute Unified Device Architecture (CUDA). The processing time remains fixed regardless of the input size and the number of optical constraints and conflicting objectives. Our OBG solver benefits of the joint use of advanced ILP-MO optimization methods, ANN large-scale inherent parallelism and CUDA-GPU High-Performance Computing (HPC) architecture. Through a comprehensive simulation study, we show that, our proposed grooming approach can significantly improve the optical traffic performance, resulting in less contention and low burst lost probability. [ABSTRACT FROM AUTHOR]

We study the parallelized ant system algorithm solving the traveling salesman problem on n cities. First, following the series of recent results for the graphics processing unit, we show that they translate to the PRAM (parallel random access machine) model. In addition, we develop a novel pheromone matrix update method under the PRAM CREW (concurrent-read exclusive-write) model and translate it to the graphics processing unit without atomic instructions. As a consequence, we give new asymptotic bounds for the parallel ant system, resulting in step complexities O(n lglg n) on CRCW (concurrent-read concurrent-write) and O(n lg n) on CREW variants of PRAM using n2 processors in both cases. Finally, we present an experimental comparison with the currently known pheromone matrix update methods on the graphics processing unit and obtain encouraging results. [ABSTRACT FROM AUTHOR]

In order to avoid the complex explicit feature extraction process and the problem of low-level data operation involved in traditional facial expression recognition, we proposed a method of Faster R-CNN (Faster Regions with Convolutional Neural Network Features) for facial expression recognition in this paper. Firstly, the facial expression image is normalized and the implicit features are extracted by using the trainable convolution kernel. Then, the maximum pooling is used to reduce the dimensions of the extracted implicit features. After that, RPNs (Region Proposal Networks) is used to generate high-quality region proposals, which are used by Faster R-CNN for detection. Finally, the Softmax classifier and regression layer is used to classify the facial expressions and predict boundary box of the test sample, respectively. The dataset is provided by Chinese Linguistic Data Consortium (CLDC), which is composed of multimodal emotional audio and video data. Experimental results show the performance and the generalization ability of the Faster R-CNN for facial expression recognition. The value of the mAP is around 0.82. [ABSTRACT FROM AUTHOR]

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of processing cores in a single chip, opening a widely range of possible applications. Here, we design a novel evolutionary computing GPU parallel function evaluation mechanism, in which different parts of time series are evaluated by different processing threads. By applying a metaheuristics fuzzy model in a low-frequency data for household electricity demand forecasting, results suggested that the proposed GPU learning strategy is scalable as the number of training rounds increases. [ABSTRACT FROM AUTHOR]

Abstract: In spite of the growing computational power offered by the commodity hardware, fast pump scheduling of complex water distribu- tion systems is still a challenge. In this paper, the Artificial Neural Network (ANN) meta-modeling technique has been employed with a Genetic Algorithm (GA) for simultaneously optimizing the pump operation and the tank levels at the ends of the cycle. The generalized GA+ANN algorithm has been tested on a real system in the UK. Comparing to the existing operation, the daily cost is reduced by about 10 − 15%, while the number of pump switches are kept below 4 switches-per-day. In addition, tank levels are optimized ensure a periodic behavior, which results in a predictable and stable performance over repeated cycles. [Copyright &y& Elsevier]

There are two major ways to implement depth estimation, multiple image depth estimation and single image depth estimation, respectively. The former has a high hardware cost because it uses multiple cameras but it has a simple software algorithm. Conversely, the latter has a low hardware cost but the software algorithm is complex. One of the recent trends in this field is to make a system compact, or even portable, and to simplify the optical elements to be attached to the conventional camera. In this paper, we present an implementation of depth estimation with a single image using a graphics processing unit (GPU) in a desktop PC, and achieve real-time application via our evolutional algorithm and parallel processing technique, employing a compute shader. The methods greatly accelerate the compute-intensive implementation of depth estimation with a single view image from 0.003 frames per second (fps) (implemented in MATLAB) to 53 fps, which is almost twice the real-time standard of 30 fps. In the previous literature, to the best of our knowledge, no paper discusses the optimization of depth estimation using a single image, and the frame rate of our final result is better than that of previous studies using multiple images, whose frame rate is about 20fps. [ABSTRACT FROM AUTHOR]

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Computerized tomography (CT) forward projection is a simulation of CT data acquisition, which can not only be used to produce projection data, but also is a key part of the iterative algorithms for CT image reconstruction. The forward projection requires both excessive computation and time consuming in cone-beam scanning mode. For improving traditional forward projection algorithms, a graphics processing unit (GPU) based cone beam CT forward projection algorithm is proposed. The algorithm generates forward projection image rapidly by calculating the projections on detector of all X-rays in parallel. Since the algorithm supports the calculation at full floating point precision and adopts tri-linear interpolation, the high-precision projection image can be achieved. The advantages of this algorithm were verified by the experiments of the projection calculation of Shepp-Logan phantom and by the comparison with other forward projection algorithms. [ABSTRACT FROM AUTHOR]

A graphics processing unit (GPU) based cone-beam CT iteration algorithm is proposed. This algorithm takes 3D texture as the discrete model of reconstructed object, h includes several base modules: the forward-projection based on ray casting, the backward-projection by backward mapping projections to each slice of object 3D texture, and the image correction and projection correction by multi-texture blending. Compared with classical TMA-SART algorithm, the proposed algorithm has a faster reconstruction speed in full floating point precision, requires less video memory, and is easier to incorporate prior knowledge and constraints into its framework. These advantages of the algorithm are verified by the experiment of the 3D image iterative reconstruction of Shepp-Logan phantom. [ABSTRACT FROM AUTHOR]

The implementation of a two-dimensional Euler solver on graphics hardware is described. The graphics processing unit is highly parallelized and uses a programming model that is well suited to flow computation. Results for a transonic turbine cascade test-case are presented. For large grids (106 nodes) a 40 times speed-up compared with a Fortran implementation on a contemporary CPU is observed. [ABSTRACT FROM AUTHOR]