An Efficient WCET-Aware Hybrid Global Branch Prediction Approach

Background: In a parallel processor, the pipeline cannot fetch the conditional instructions with the next clock cycle, leading to a pipeline stall. So, conditional instructions create a problem in the pipeline because the proper path can only be known after the branch execution. To accurately predict branches, a significant predictor is proposed for the prediction of conditional branch instruction. Method: In this paper, a single branch prediction and a correlation branch prediction scheme are applied to the different trace files by using the concept of saturating counters. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. Results: Firstly, a single branch prediction and correlation branch prediction technique are applied to the trace files using saturating counters. By comparison, it can be observed that a correlation branch prediction technique provides better results by enhancing the accuracy rate of 2.25% than the simple branch prediction. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. The obtained results suggest that the proposed hybrid branch prediction schemes provide an increased accuracy rate of 3.68% and 1.43% than single branch prediction and correlation branch prediction. Conclusion: The proposed hybrid branch prediction scheme gives a lower misprediction rate and higher accuracy rate than the simple branch prediction scheme and correlation branch prediction scheme.

The Impact of Branch Direction History Combined with Global Branch History in Branch Prediction

IEICE Transactions on Information and Systems ◽

10.1093/ietisy/e88-d.7.1754 ◽

2005 ◽

Vol E88-D (7) ◽

pp. 1754-1758 ◽

Cited By ~ 2

Author(s):

J. W. KWAK

Keyword(s):

Branch Prediction ◽

The Impact ◽

Global Branch

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

Revista de Chimie ◽

10.37358/rc.17.4.5566 ◽

2017 ◽

Vol 68 (4) ◽

pp. 858-863

Author(s):

Mihaela Oprea ◽

Marius Olteanu ◽

Radu Teodor Ianache

Keyword(s):

Air Pollution ◽

Early Warning ◽

Urban Areas ◽

Early Warning System ◽

Urban Air Pollution ◽

Warning System ◽

Air Pollutant ◽

Pollution Level ◽

Urban Air ◽

Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

An Affect Prediction Approach Through Depression Severity Parameter Incorporation in Neural Networks

10.21437/interspeech.2017-120 ◽

2017 ◽

Cited By ~ 5

Author(s):

Rahul Gupta ◽

Saurabh Sahu ◽

Carol Espy-Wilson ◽

Shrikanth S. Narayanan

Keyword(s):

Neural Networks ◽

Depression Severity ◽

Prediction Approach ◽

Severity Parameter

A Statistical Seizure Prediction Approach Based on Savitzky-Golay Smoothing

Menoufia Journal of Electronic Engineering Research ◽

10.21608/mjeer.2018.64386 ◽

2018 ◽

Vol 27 (1) ◽

pp. 53-70

Author(s):

Ahmed Sedik ◽

Turky Alotaiby ◽

Heba El-Khobby ◽

Mahmoud Atea ◽

Saleh A. Alshebeili ◽

...

Keyword(s):

Seizure Prediction ◽

Prediction Approach

Quantitative Evaluation of Drug-Drug Interaction Potentials by in vivo Information- Guided Prediction Approach

Current Drug Metabolism ◽

10.2174/1389200216666150223151758 ◽

2015 ◽

Vol 15 (8) ◽

pp. 761-766 ◽

Cited By ~ 3

Author(s):

Feng Chen ◽

Zhe-Yi Hu ◽

Wei-Wei Jia ◽

Jing-Tao Lu ◽

Yuan-Sheng Zhao

Keyword(s):

Drug Interaction ◽

Quantitative Evaluation ◽

Interaction Potentials ◽

Drug Drug Interaction ◽

Prediction Approach

A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering

IEEE Access ◽

10.1109/access.2019.2897979 ◽

2019 ◽

Vol 7 ◽

pp. 19550-19563 ◽

Cited By ~ 9

Author(s):

Ling Huang ◽

Chang-Dong Wang ◽

Hong-Yang Chao ◽

Jian-Huang Lai ◽

Philip S. Yu

Keyword(s):

Collaborative Filtering ◽

Prediction Approach

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1573-2017 ◽

2017 ◽

Vol 21 (3) ◽

pp. 1573-1591 ◽

Cited By ~ 19

Author(s):

Louise Crochemore ◽

Maria-Helena Ramos ◽

Florian Pappenberger ◽

Charles Perrin

Keyword(s):

Long Range ◽

Precipitation Forecast ◽

Low Flow ◽

Drought Risk ◽

Streamflow Forecasting ◽

Streamflow Forecasts ◽

Precipitation Indices ◽

Seasonal Streamflow ◽

Prediction Approach ◽

The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

Throughput Performance Prediction Approach for Wi-Fi Site Surveys

2021 IEEE Radio and Wireless Symposium (RWS) ◽

10.1109/rws50353.2021.9360357 ◽

2021 ◽

Author(s):

Akihiro Tatsuta ◽

Yasunori Shimazaki ◽

Teppei Emura ◽

Takuya Asada ◽

Taichi Hamabe

Keyword(s):

Performance Prediction ◽

Throughput Performance ◽

Prediction Approach

Current and New Approaches to Predict the Deflections of One-Way Flexural Members with a Focus on Composite Steel Deck Slabs Voided by Circular Tubes

Materials ◽

10.3390/ma14020421 ◽

2021 ◽

Vol 14 (2) ◽

pp. 421

Author(s):

Chang-Hwan Lee ◽

Iman Mansouri ◽

Jaehoon Bae ◽

Jaeho Ryu

Keyword(s):

Structural Function ◽

Flexural Members ◽

Long Span ◽

Deck Slabs ◽

New Type ◽

New Perspective ◽

Flexural Tests ◽

Prediction Approach ◽

Steel Deck

A new type of composite voided slab, the TUBEDECK (TD), which utilizes the structural function of profiled steel decks, has recently been proposed. Previous studies have confirmed that the flexural strength of TD slabs can be calculated based on the full composite contribution of the steel deck, but for long-span flexural members, the deflection serviceability requirement is often dominant. Herein, we derived a novel deflection prediction approach using the results of flexural tests on slab specimens, focusing on TD slabs. First, deflection prediction based on modifications of the current code was proposed. Results revealed that TD slabs exhibited smaller long-term deflections and at least 10% longer maximum span lengths than solid slabs, indicating their greater efficiency. Second, a novel rational method was derived for predicting deflections without computing the effective moment of inertia. The ultimate deflections predicted by the proposed method correlated closely with the deflection under maximum bending moments. To calculate immediate deflections, variation functions for the concrete strain at the extreme compression fiber and neutral axis depth were assumed with predictions in good agreement with experiments. The proposed procedure has important implications in highlighting a new perspective on the deflection prediction of reinforced concrete and composite flexural members.