슈퍼 스칼라 프로세서에서 중복을 피할 수 있는 하이브리드 Value 예상방법

조영일 수원대학교 자연과학연구소 1999 자연과학논문집 Vol.2 No.-

Value prediction is a technique that breaks true data dependences by predicting the outcome of an instruction and executes speculatively its data-dependent instructions based on the predicted outcome. Among various predictors, last value predictor and stride-based predictor have low hardware cost, but they have low prediction accuracy. On the other hand, two-level value predictor obtains high prediction accuracy, but it has high hardware cost. Also, hybrid value predictors can obtain high prediction accuracy using advantages of various value predictors, but they have a defect that same instruction uses multiple entries of various predictors. This paper presents an non-duplicated hybrid value predictor which can dynamically select the most adequate value predictor for a fetched instruction and avoid the duplication to be allocated multiple entries to same instruction. We use execution-driven simulation to study the prediction rate and the prediction accuracy of the proposed hybrid value predictor using SPECint95 benchmarks.

Value 예상을 위한 하이브리드 예상 메커니즘

조영일 수원대학교 자연과학연구소 1998 자연과학논문집 Vol.1 No.-

Value prediction attempts to eliminate true-data dependencies by dynamically predicting the outcome values of instructions and executing true-data dependent instructions based on that prediction. Any single predictor can't get high prediction accuracies for all instructions. For some instructions, last-value predictor gives good predictor accuracy. On the other hand, for other instructions, stride-based value predictor gives good predictor accuracy. This paper presents a hybrid value predictor which reduces the hardware cost, compared to stride-based value predictor, and improves the predictor accuracy, compared to last-value predictor.

Predictors of Dyslexia for Korean-English Bilinguals in the U.S.: A Literature Review

Jaran Shin 고려대학교 언어정보연구소 2013 언어정보 Vol.16 No.-

Identifying bilingual children’s dyslexia can be challenging because teasing apart their L2 reading developmental progress from signals of dyslexia is not easy. Although Koreans rank as one of the largest immigrant groups in the U.S. and a greater number of Korean children has recently flowed into the country, only a handful of studies have examined Korean children’s English reading profiles and L2 dyslexic patterns. Thus, this paper reviews the literature on predictors of dyslexia for Korean-English bilingual children by discussing the characteristics of Korean and English, examining predictors of dyslexia in monolingual Korean and English speakers, and comparing and contrasting those predictors. This piece will help to: (a) understand potential challenges that Korean children learning English may face, (b) suggest more reliable ways to assess their reading performance and diagnose their dyslexia in L2 (i.e. English), and (c) envision ways to support them at the societal level.

Cognitive-Communicative Predictors of Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

Mi Sook Lee 한국청각언어재활학회 2020 Audiology and Speech Research Vol.16 No.4

Mild cognitive impairment (MCI) is the preclinical stage and sign of dementia. It is also important for guidance in the prevention and intervention of neurological disease. The purpose of this study was to review literatures on cognitive/communicative and other predictors of MCI patients systematically, and to propose the evidence-based data including effect sizes of them using a meta-analysis method. Fifty-seven researches published since 2010, meeting the inclusion and exclusion criteria, were entered into the analysis. They were analyzed in a methodological and content level, and the effect sizes were calculated by 3 predictors. Predictive values were pooled from cognitive (10 domains), communicative (9 domains), and other (3 domains). The main findings were as follows. Firstly, the general target population for studies was older adults over the age of 55, and most studies included at least 2 types of predictors. Secondly, average effect sizes of 3 predictors in MCI were all significant. Thirdly, cognitive predictors like memory and general cognition had significant and high-level effect sizes. Fourthly, communicative predictors including comprehension and word fluency had moderate-level effect sizes significantly. Lastly, all demographic and neuropsychological (age, education, depression) predictors had significant and moderate-level effect sizes. Our results provide the evidence-based information to predict MCI. Especially, specific cognitive and communicative predictors may contribute to increase the diagnostic and prognostic accuracy in MCI. This study is also expected to present clinically available data and increase the effect in intervention for MCI.

Clinical Predictors of High Posttreatment Platelet Reactivity to Clopidogrel in Koreans

Park, Kyung Woo,Park, Jin Joo,Jeon, Ki‐,Hyun,Kang, Si‐,Hyuk,Oh, Il‐,Young,Yang, Han‐,Mo,Cho, Hyun‐,Jai,Lee, Hae‐,Young,Kang, Hyun‐,Jae,Koo, Bon‐,Kwon,Oh Blackwell Publishing Ltd 2012 CARDIOVASCULAR THERAPEUTICS Vol.30 No.1

<P><B>SUMMARY</B></P><P><B>Introduction:</B> High posttreatment platelet reactivity to clopidogrel (HPPR) is associated with major adverse cardiac events. However, the clinical predictors of HPPR in Asians have not been studied previously.</P><P><B>Aims:</B> We sought to determine clinical predictors of HPPR in Koreans.</P><P><B>Results:</B> We measured platelet reactivity with the VerifyNow P2Y12 assay in 1431 consecutive patients undergoing coronary angiography. We used the cut‐off value of greater than 275 P2Y12 Reaction Unit (PRU) to define patients with HPPR. The clinical characteristics were compared between patients with HPPR (36.3%) and those without HPPR (63.7%).</P><P>The mean age (65.4 ± 9.1 vs. 62.2 ± 9.7 years) was higher, hypertension (68.5% vs. 62.0%), diabetes mellitus (35.4% vs. 28.5%), chronic kidney disease (36.3% vs. 22.5%), renal replacement treatment (1.2% vs. 0.2%), and congestive heart failure (1.3% vs. 0.3%) were more common among patients with HPPR, while male gender (72.6% vs. 54.8%) and smoking (19.9% vs. 13.1%) were more common among non‐HPPR patients. Mean glomerular filtration rate (63.5 ± 18.6 vs. 69.7 ± 16.1 mL/min/1.73 m<SUP>3</SUP>) was lower and C‐reactive protein (hs‐CRP) (6.6 ± 20.5 mg/L vs. 4.2 ± 12.1 mg/L) level was higher among those with HPPR. Independent predictors of HPPR were female gender (OR 1.90, <I>P</I>≤ 0.001), chronic kidney disease (OR 1.51, 0 = 0.004), diabetes mellitus (OR 1.35, <I>P</I>= 0.024), hs‐CRP ≥ 2.0 mg/L (OR 1.31, <I>P</I>= 0.005), and age increase in decades (OR 1.21, <I>P</I>= 0.002), while smoking was negative risk factor (OR 0.63, <I>P</I>= 0.015). The number of risk factors was linearly associated with the risk of HPPR, with most patients having one or two predictors.</P><P><B>Conclusion:</B> In Korean population, independent clinical predictors of HPPR included diabetes mellitus, increased age, female gender, chronic kidney disease, and hs‐CRP ≥ 2.0 mg/L, while cigarette smoking was associated with better responsiveness. Mean platelet reactivity and HPPR prevalence steadily increased with the number of clinical predictors.</P>

A Predictor-Corrector Method for the Structural Nonlinear Analysis

Kim, Jong-Hoon,Kim, Yong-Hyup 서울대학교 항공우주신기술연구소 2000 항공우주신기술연구소 연구보고 Vol.1 No.2

A predictor-corrector method is presented for the efficient and reliable analysis of structural nonlinear behaviors. The key idea lies on modifying the starting point of iterations of the Newton iterative method. The conventional Newton method starts iterations at the previously converged solution point. However, in the present predictor-corrector method, a point close to the converged solution of the current step is predicted first, and then the Newton method starts iterative procedure at the predicted point. The predictor, the neural network in the present study, recognizes the pattern of the previously converged solutions to predict the starting point of the current step. Then the corrector, the standard Newton method in the present study, is used to obtain the converged solution iteratively, starting at the predicted point. Numerical tests are conducted to demonstrate the effectiveness and reliability of the present predictor-corrector method. The performance of the present method is compared with the conventional Newton method and Riks' continuation method. The present predictor-corrector method saves computational cost significantly and yields stable results without diverging, for the nonlinear analysis with monotonous deformation path as well as complicated deformation path including buckling and post buckling behaviors.

Design of a G-Share Branch Predictor for EISC Processor

Kim, InSik,Jun, JaeYung,Na, Yeoul,Kim, Seon Wook The Institute of Electronics and Information Engin 2015 IEIE Transactions on Smart Processing & Computing Vol.4 No.5

This paper proposes a method for improving a branch predictor for the extendable instruction set computer (EISC) processor. The original EISC branch predictor has several shortcomings: a small branch target buffer, absence of a global history, a one-bit local branch history, and unsupported prediction of branches following LERI, which is a special instruction to extend an immediate value. We adopt a G-share branch predictor and eliminate the existing shortcomings. We verified the new branch predictor on a field-programmable gate array with the Dhrystone benchmark. The newly proposed EISC branch predictor also accomplishes higher branch prediction accuracy than a conventional branch predictor.

Cascade Integral Predictors and Feedback Control for Nonlinear Systems with Unknown Time-varying Input-delays

Kanghui He,Chao-Yang Dong,Qing Wang 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.5

In this paper, we consider the problem of predictor design for nonlinear systems in the presence of unknown time-varying input-delays. A cascade integral high-gain predictor is proposed to estimate the future state. With a distinctive structure, the predictor can handle unknown delays and eliminate the “peaking phenomenon” during the transient period. Then, a predictor-based output feedback control is designed to guarantee the boundedness of system states. Lyapunov-Krasovskii functional and perturbation theories are used to prove the convergence of the estimation error and the closed-loop system. Finally, simulation results illustrate the superior performance of the cascade integral predictor compared to the standard high-gain predictor.

뇌혈관질환의 예측인자로서의 악력

정석환,김재현 한국보건행정학회 2019 보건행정학회지 Vol.29 No.3

Background: Cerebrovascular disease is included in four major diseases and is a disease that has high rates of prevalence and mortality around the world. Moreover, it is a disease that requires a high cost for long-term hospitalization and treatment. This study aims to figure out the correlation between grip strength, which was presented as a simple, cost-effective, and relevant predictor of cerebrovascular disease, and cerebrovascular disease based on the results of a prior study. And furthermore, our study compared model suitability of the model to measuring grip strength and relative grip strength as a predictor of cerebrovascular disease to improve the quality of cerebrovascular disease’s predictor. Methods: This study conducted an analysis based on the generalized linear mixed model using the data from the Korea Longitudinal Study of Ageing from 2006 to 2016. The research subjects consisted of 9,132 middle old age people aged 45 years or older at baseline with no missing information of education level, gender, marital status, residential region, type of national health insurance, self-related health, smoking status, alcohol use, and economic activity. The grip strength was calculated the average which measured 4 times (both hands twice), and the relative grip force was divided by the body mass index as a variable considering the anthropometric figure that affects the cerebrovascular disease and the grip strength. Cerebrovascular diseases, a dependent variable, were investigated based on experiences diagnosed by doctors. Results: An analysis of the association between grip strength and found that about 0.972 (odds ratio [OR], 0.972; 95% confidence interval [CI], 0.963–0.981) was the incidence of cerebral vascular disease as grip strength increased by one unit increase and the association between relative grip strength and cerebrovascular disease found that about 0.418 (OR, 0.418; 95% CI, 0.342–0.511) was the incidence of cerebral vascular disease as relative grip strength increased by unit. In addition, the model suitability of the model for each grip strength and relative grip strength was 11,193 and 11,156, which means relative grip strength is the better application to the predictor of cerebrovascular diseases, irrespective of other variables. Conclusion: The results of this study need to be carefully examined and validated in applying relative grip strength to improve the quality of predictors of cerebrovascular diseases affecting high mortality and prevalence.

비선형 하중제어 모델의 예측기 설계 및 알고리즘 구현을 위한 수치연산 오차 분석과 평가

왕현민(Hyun-Min Wang),우광준(Kwang-Joon Woo) 大韓電子工學會 2009 電子工學會論文誌-SC (System and control) Vol.46 No.6

운동하는 물체를 제어하기 위한 제어이론은 디지털 컴퓨터(임베디드시스템)를 이용하여 복잡한 신경망 이론, 인공지능 이론, 비선형 모델 예측 제어 이론등이 제어기 설계 단계에서 구현되고 있다. 비행제어 시스템의 비선형 모델 예측 제어 예측기는 구현하는 컴퓨터의 성능과 각종 모듈의 응용프로그램을 하드실시간(Hard Real-Time)으로 처리할 수 있도록 응답 시간을 충족하여야 한다. 이와 동시에 제어 시스템에의 성능을 충분히 발휘할 수 있는 정확성도 고려하여야 한다. 수학적 영역에서의 오류는 전체 알고리즘 구현에 영향을 준다. 그러나 이러한 수학적 오류 발생 요인은 예측기에서 생성되는 파라미터에서 최종 정확도 계산에 가끔 고려하지 않는다. 본 논문에서는 비행체 제어를 위한 디지털 제어 시스템에서 하드실시간 하중제어 모델 예측기를 구현하고, 알고리즘의 응답시간을 살펴본다. 또한 이에 따른 정밀도를 보장하는 고효율 예측기를 구현하는 알고리즘을 살펴본다. 예측기는 하중 제어 모델에서 오일러 방법, Heun 방법, Runge-kutta 방법, 테일러 방법의 수치적분 알고리즘을 사용하여 구현된다. For the shake of control for movement object, control theory like neural network, nonlinear model predictive control(NMPC) is realized on digital high speed computer. Predictor of flight control system(FCS) based nonlinear model predictive control has to be satisfied with response for hard real-time to perform applications on each module in the FCS. Simultaneously, It gives a serious consideration accuracy to give full play to FCS's performance. Error of mathematical aspect affects realization of whole algorithm. But factors of bring mathematical error is not considered to calculate final accuracy on parameter of predictor. In this paper, Predictor was made using load control model on the digital computer for design FCS at hard real-time and is shown response time on realization algorithm. And is shown realization algorithm of high effective predictor over the accuracy. The predictor was realized on the load control model using Euler method, Heun method, Runge-Kutta and Taylor method.