2024, Vol.29, No.6

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

Deep learning with faster and more accurate results requires large amounts of storage space and large computations. Accordingly, many studies are using hardware accelerators for quick and accurate calculations. However, the performance bottleneck is due to data movement between the hardware accelerators and the CPU. In this paper, we propose a data prefetch strategy that can efficiently reduce such operational bottlenecks. The core idea of the data prefetch strategy is to predict the data needed for the next task and upload it to local memory while the hardware accelerator (Matrix Multiplication Unit, MMU) performs a task. This strategy can be enhanced by using a dual buffer to perform read and write operations simultaneously. This reduces latency and execution time of data transfer. Through simulations, we demonstrate a 24% improvement in the performance of hardware accelerators by maximizing parallel processing with dual buffers and bottlenecks between memories with data prefetch.

There is a need for and positive aspects of article-based advertising, but as exaggerated and disguised information is delivered due to some indiscriminate 'article-based advertisements', readers have difficulty distinguishing between general articles and article-based advertisements, leading to a lot of misinterpretation and confusion of information. is doing Since readers will continue to acquire new information and apply this information at the right time and place to bring a lot of value, it is judged to be even more important to distinguish between accurate general articles and article-like advertisements. Therefore, as differentiated information between general articles and article-like advertisements is needed, as part of this, for readers who have difficulty identifying accurate information due to such indiscriminate article-like advertisements in Internet newspapers, this paper introduces IT and AI technologies. We attempted to present a method that can be solved in terms of a system that incorporates, and this method was designed to extract articleable advertisements using a knowledge-based natural language processing method that finds and refines advertising keywords and deep learning technology.

In this paper, we propose a fast GPU-based method for representing pore flow, absorption, emission, and diffusion effects represented by cloth-liquid interactions using smoothed particle hydrodynamics (SPH), a particle-based fluid solver: 1) a unified framework for GPU-based representation of various physical effects represented by cloth-liquid interactions; 2) a method for efficiently calculating the saturation of a node based on SPH and transferring it to the surrounding porous particles; 3) a method for improving the stability based on Darcy's law to reliably calculate the direction of fluid absorption and release; 4) a method for controlling the amount of fluid absorbed by the porous particles according to the direction of flow; and finally, 5) a method for releasing the SPH particles without exceeding their maximum mass. The main advantage of the proposed method is that all computations are computed and run on the GPU, allowing us to quickly model porous materials, porous flows, absorption, reflection, diffusion, etc. represented by the interaction of cloth and fluid.