• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.1-11
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• 2023

The emergence of generative hyperscale artificial intelligence (AI) has enabled new services, such as image-generating AI and conversational AI based on large language models. Such services likely lead to the influx of numerous users, who cannot be handled using conventional AI models. Furthermore, the exponential increase in training data, computations, and high user demand of AI models has led to intensive hardware resource consumption, highlighting the need to develop domain-specific semiconductors for hyperscale AI. In this technical report, we describe development trends in technologies for hyperscale AI processors pursued by domestic and foreign semiconductor companies, such as NVIDIA, Graphcore, Tesla, Google, Meta, SAPEON, FuriosaAI, and Rebellions.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.485-494
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• 2008

Huge amounts of various web items such as keywords, images, and web pages are being made widely available on the Web. The popularities of such web items continuously change over time, and mining temporal patterns in popularities of web items is an important problem that is useful for several web applications. For example, the temporal patterns in popularities of search keywords help web search enterprises predict future popular keywords, enabling them to make price decisions when marketing search keywords to advertisers. However, presence of millions of web items makes it difficult to scale up previous techniques for this problem. This paper proposes an efficient method for mining temporal patterns in popularities of web items. We treat the popularities of web items as time-series, and propose gapmeasure to quantify the similarity between the popularities of two web items. To reduce the computation overhead for this measure, an efficient method using the Fast Fourier Transform (FFT) is presented. We assume that the popularities of web items are not necessarily following any probabilistic distribution or periodic. For finding clusters of web items with similar popularity trends, we propose to use a density-based clustering algorithm based on the gap measure. Our experiments using the popularity trends of search keywords obtained from the Google Trends web site illustrate the scalability and usefulness of the proposed approach in real-world applications.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.2
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• pp.164-172
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• 2021

Purpose: The purpose of this study was to analyze the level of interest that common Internet users have in dental implant using a Google Trends, and to compare the level of interest with big data from National Health Insurance Service. Materials and methods: Google Trends provides a relative search volume for search keywords, which is the average data that visualizes the frequency of searches for those keywords over a specific period of time. Implant was selected as the search keyword to evaluate changes in time flows of general Internet users' interest from 2015 to 2019 with trend line and 6 month moving average. Relative search volume for implant was analyzed with the number of patients who received National Health Insurance coverage for implant. Interest in implant and conventional denture was compared and popular related search keywords were analyzed. Results: Relative search volume for implant has increased gradually and showed a significant positive correlation with the total number of patients (P<.01). Interest in implant was higher than denture for most of the time. Keywords related to implant cost were most frequently observed in all years and related search on implant procedure was increasing. Conclusion: Within the limitations of this study, the public interest in dental implant was gradually increasing and specific areas of interest were changing. Web-based Google Trends data was also compared with traditional data and significant correlation was confirmed.

• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.121-134
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• 2018

The Von Neumann based architecture of the modern computer has dominated the computing industry for the past 50 years, sparking the digital revolution and propelling us into today's information age. Recent research focus and market trends have shown significant effort toward the advancement and application of artificial intelligence technologies. Although artificial intelligence has been studied for decades since the Turing machine was first introduced, the field has recently emerged into the spotlight thanks to remarkable milestones such as AlexNet-CNN and Alpha-Go, whose neural-network based deep learning methods have achieved a ground-breaking performance superior to existing recognition, classification, and decision algorithms. Unprecedented results in a wide variety of applications (drones, autonomous driving, robots, stock markets, computer vision, voice, and so on) have signaled the beginning of a golden age for artificial intelligence after 40 years of relative dormancy. Algorithmic research continues to progress at a breath-taking pace as evidenced by the rate of new neural networks being announced. However, traditional Von Neumann based architectures have proven to be inadequate in terms of computation power, and inherently inefficient in their processing of vastly parallel computations, which is a characteristic of deep neural networks. Consequently, global conglomerates such as Intel, Huawei, and Google, as well as large domestic corporations and fabless companies are developing dedicated semiconductor chips customized for artificial intelligence computations. The AI Processor Research Laboratory at ETRI is focusing on the research and development of super low-power AI processor chips. In this article, we present the current trends in computation platform, parallel processing, AI processor, and super-threaded AI processor research being conducted at ETRI.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1004-1022
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• 2022

This study seeks to investigate K-fashion's external image by examining the trends in global media reporting. It applies Dynamic Topic Modeling (DTM), which captures the evolution of topics in a sequentially organized corpus of documents, and consists of text preprocessing, the determination of the number of topics, and a timeseries analysis of the probability distribution of words within topics. The data set comprised 551 online media articles on 'Korean fashion' or 'K-fashion' published on Google News between 2010 and 2021. The analysis identifies seven topics: 'brand look and style,' 'lifestyle,' 'traditional style,' 'Seoul Fashion Week (SFW) event,' 'model size,' 'K-pop,' and 'fashion market,' as well as annual topic proportion trends. It also explores annual word changes within the topic and indicates increasing and decreasing word patterns. In most topics, the probability distribution of the word 'brand' is confirmed to be on the increase, while 'digital,' 'platform,' and 'virtual' have been newly created in the 'SFW event' topic. Moreover, this study confirms the transition of each K-fashion topic over the past 12 years, along with various factors related to Hallyu content, traditional culture, government support, and digital technology innovation.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.116-126
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• 2022

Many studies using trademark information have suggested that trademark information is good data to monitor business trends. This study intends to analyze the trend change before and after COVID-19 using trademark information. Changes before and after COVID-19 were analyzed by using goods & service classification, similar group code, and designated goods information as trademark information. Among the trademark information, it was statistically significant that the change in trends before and after COVID-19 using designated goods names. To verify the results, the changes in keywords using designated goods names before and after COVID-19 were compared with the frequency of keywords in Google Trends. Among the top 8 keywords extracted from designated goods names, the frequency of Google trend searches for 'online, antibacterial, prevention of epidemics, meal kit, virtual' is on the rise, and 'mask, droplet' is not on the rise, but it increased rapidly at the time of COVID-19, and even after COVID-19, it showed a higher level than before. The frequency of 'unmanned' does not differ much before and after COVID-19, but it has been maintained at a consistently high level, and related businesses have been active since before COVID-19, and it can be interpreted as a keyword with high public interest. This study has academic achievements in that it specifically identified information that could be used in business trends by using three types of trademark information.

• Electronics and Telecommunications Trends
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• v.29 no.4
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• pp.30-38
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• 2014

최근의 플랫폼 기술동향은 웹 기반 혹은 단순 의사소통이 가능한 모바일 플랫폼에서 빅데이터와 인공지능기술이 접목되면서 심층 질의응답이 가능한 차세대 지능형 지식처리 플랫폼으로의 진화가 진행 중이다. 선진국에서는 국가 차원 혹은 글로벌 기업의 주도하에 대형 장기 프로젝트가 진행 중이다. 국가 주도의 프로젝트로는 미국의 PAL, 유럽의 Human Brain, 일본의 Todai 프로젝트가 대표적인 예이며, 글로벌 기업의 경우는 IBM의 Watson, Google의 Knowledge Graph, Apple의 Sir가 대표적인 예이다. 본고에서는 차세대 지능형 플랫폼의 핵심기술인 인간과 기계의 지식소통을 위한 빅데이터 기반의 지식처리 인공지능 소프트웨어 기술의 개념과 국내외 기술 및 산업, 지식재산권 동향 등을 살펴보고 산업계 활용방안 및 발전방향에 대해 논하고자 한다.

• Electronics and Telecommunications Trends
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• v.23 no.2 s.110
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• pp.69-79
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• 2008

지난 수 년간 이동통신 시장은 그야말로 급격한 변화와 성장을 경험하였다. 특히 휴대폰 시장은 이미 성숙기를 지나 교체수요 시장으로 선회하고 있으며, 기술적 측면에서는 2.5세대(CDMA 1x, GPRS), 3세대(EV-DO, W-CDMA), 3.5세대(HSDPA)로 빠르게 이동하고 있다. 최근 이동통신단말기 분야에서 일어나고 있는 변화의 핵심 요소로 이동통신단말기 산업과 모바일 인터넷 콘텐츠 산업과의 융합 현상을 들 수 있다. 예를 들어 Google의 Android(플랫폼), 마이크로소프트의 Windows Mobile(mobile OS) 등 이 큰 관심을 끌고 있으며, 스마트폰 시장의 형성과 터치스크린폰의 확대 등이 예견되고 있다. 본 고에서는 이러한 융합 현상에 따른 이동통신단말기 산업 및 시장의 일반적 현황을 소개하고, 이동통신단말기와 직접 관련된 단말기의 외관적 요소, User Interface, Mobile OS 및 Platform 등을 중심으로 최근 동향 및 향후 진화방향에 대해 살펴본다.

• Electronics and Telecommunications Trends
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• v.24 no.4
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• pp.1-13
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• 2009

본 논문은 클라우드 컴퓨팅 기술 동향에 대한 분석서로 관련 기술과 주요 서비스 내용을 소개한다. 2006년 클라우드 컴퓨팅이란 용어가 처음 생겨난 이래, 2008년 글로벌 IT 기업 CEO들이 잇달아 차기 비즈니스의 핵심 기술로 클라우드 컴퓨팅을 지목한 이후, 전세계가 클라우드 컴퓨팅에 대한 관심이 집중되고 있다. 이에 클라우드 컴퓨팅의 대표 서비스 형태인 IaaS, PaaS, SaaS 서비스에 대한 개념을 설명하고, 각 서비스별 대표 기업들의 기술 동향을 살펴본다. IaaS 서비스에 대해서는 아마존의 EC2와 S3 서비스를, PaaS 서비스에 대한 Google의 AppEngine을, SaaS 서비스로 세일즈포스닷컴의 세일즈포스 서비스를 대표 기술로 소개하고 클라우드 컴퓨팅을 구성하기 위한 기술적인 요소들을 간략하게 언급하였다.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.51-60
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• 2023

Quantum machine learning (QML) is an area of quantum computing that leverages its principles to develop machine learning algorithms and techniques. QML is aimed at combining traditional machine learning with the capabilities of quantum computing to devise approaches for problem solving and (big) data processing. Nevertheless, QML is in its early stage of the research and development. Thus, more theoretical studies are needed to understand whether a significant quantum speedup can be achieved compared with classical machine learning. If this is the case, the underlying physical principles may be explained. First, fundamental concepts and elements of QML should be established. We describe the inception and development of QML, highlighting essential quantum computing algorithms that are integral to QML. The advent of the noisy intermediate-scale quantum era and Google's demonstration of quantum supremacy are then addressed. Finally, we briefly discuss research prospects for QML.