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Relevant bibliographies by topics / Dynamic branch prediction

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Academic literature on the topic 'Dynamic branch prediction'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Dynamic branch prediction"

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Ali, S. Al-Khalid, and S. Omran Safaa. "Hybrid branch prediction for pipelined MIPS processor." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3476–82. https://doi.org/10.11591/ijece.v10i4.pp3476-3482.

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In the modern microprocessors that designed with pipeline stages, the performance of these types of processors will be affected when executing branch instructions, because in this case there will be stalls in the pipeline. In turn this causes in reducing the Cycle Per Instruction (CPI) of the processor. In the case of executing a branch instruction, the processor needs an extra clocks to know if that branch will happen (Taken) or not (Not Taken) and also it requires calculating the new address in the case of the branch is Taken. The prediction that the branch is T / NT is an important stage in

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Mohammadi, Milad, Song Han, Tor M. Aamodt, and William J. Dally. "On-Demand Dynamic Branch Prediction." IEEE Computer Architecture Letters 14, no. 1 (2015): 50–53. http://dx.doi.org/10.1109/lca.2014.2330820.

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Jiménez, Daniel A., and Calvin Lin. "Neural methods for dynamic branch prediction." ACM Transactions on Computer Systems 20, no. 4 (2002): 369–97. http://dx.doi.org/10.1145/571637.571639.

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Silc, Jurij, Theo Ungerer, and Borut Robic. "Dynamic branch prediction and control speculation." International Journal of High Performance Systems Architecture 1, no. 1 (2007): 2. http://dx.doi.org/10.1504/ijhpsa.2007.013287.

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Pan, Shien-Tai, Kimming So, and Joseph T. Rahmeh. "Improving the accuracy of dynamic branch prediction using branch correlation." ACM SIGPLAN Notices 27, no. 9 (1992): 76–84. http://dx.doi.org/10.1145/143371.143490.

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Chen, Chen, Zhi-jian Chen, Jian-yi Meng, and Xiao-lang Yan. "Branch Prediction Based on Dynamic Polarity Transformation." Journal of Electronics & Information Technology 35, no. 4 (2014): 1001–6. http://dx.doi.org/10.3724/sp.j.1146.2012.00650.

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Nain, Sweety, and Prachi Chaudhary. "Implementation and comparison of bi-modal dynamic branch prediction with static branch prediction schemes." International Journal of Information Technology 13, no. 3 (2021): 1145–53. http://dx.doi.org/10.1007/s41870-021-00631-z.

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Kwak, Jong Wook, and Chu Shik Jhon. "Dynamic per-branch history length adjustment to improve branch prediction accuracy." Microprocessors and Microsystems 31, no. 1 (2007): 63–76. http://dx.doi.org/10.1016/j.micpro.2006.08.002.

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Hadeel, SH. Mahmood. "FPGA configuration of an alloyed correlated branch predictor used with RISC processor for educational purposes." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 265–71. https://doi.org/10.11591/ijece.v11i1.pp265-271.

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Instructions pipelining is one of the most outstanding techniques used in improving processor speed; nonetheless, these pipelined stages are constantly facing stalls that caused by nested conditional branches. During the execution of nested conditional branches, the behavior of the running branch depends on the history information of the previous ones; therefore, these branches have the greatest effect in reducing the prediction accuracy of a branch predictor among conditional branches. The purpose of this research is to reduce the stall cycles caused by correlated branches misprediction by in

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Hudoud, Ali. "Combining the Forwarding with Delay Slots Operations to Avoid the Branch Misprediction Penalty in Superscalar Processors." مجلة الجامعة الأسمرية: العلوم التطبيقية 6, no. 5 (2021): 816–27. http://dx.doi.org/10.59743/aujas.v6i5.1059.

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Using pipeline system in modern possessors has contributed significantly to the development of processors performance by increasing its speed faster than before, where CPI approaches to 1, however, this technique accompanied many problems, one of them called Branch Misprediction penalty due to control hazard, most of them occurs when implementing dynamic branch prediction. For every five jump commands of a program there is jump command, which causes interruption of the execution of orders through pipeline systems. There are many proposed previews studies, such as Dynamic Branch Prediction and

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Dissertations / Theses on the topic "Dynamic branch prediction"

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Egan, Colin. "Dynamic branch prediction in high performance superscalar processors." Thesis, University of Hertfordshire, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340035.

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Santos, Rafael Ramos dos. "DCE: the dynamic conditional execution in a multipath control independent architecture." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2003. http://hdl.handle.net/10183/5596.

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Esta tese apresenta DCE, ou Execução Dinâmica Condicional, como uma alternativa para reduzir o custo da previsão incorreta de desvios. A idéia básica do modelo apresentado é buscar e executar todos os caminhos de desvios que obedecem à certas restrições no que diz respeito a complexidade e tamanho. Como resultado, tem-se um número menor de desvios sendo previstos e consequentemente um número menor de desvios previstos incorretamente. DCE busca todos os caminhos dos desvios selecionados evitando quebras no fluxo de busca quando estes desvios são buscados. Os caminhos buscados dos desvios seleci

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Chung, Li-Wen, and 鍾立文. "A Study on Improving the Performance of Two-level Adaptive Training Branch Prediction by Dynamic Selecting Branch History." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/68297042457673283291.

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碩士<br>國立中山大學<br>電機工程研究所<br>86<br>Predict the branch outcomes correctly can avoid pipeline bubbles and thus reduce the attendant loss in performance. The accuracy of branch prediction is especially importan in the future, since the more instruction level parallelism is exploited in high-performance microprocessors the induced penalty will get worse when we make error predition. In this paper, we propose a mechanism, called X-Prediction (eXcellent Prediction), to improve the prediction acculacy and the performance in Two-Level-Adaptive Training Branch Prediction. Because it is not feasible to ha

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Zaleski, Mathew. "YETI: a GraduallY Extensible Trace Interpreter." Thesis, 2008. http://hdl.handle.net/1807/11282.

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The implementation of new programming languages benefits from interpretation because it is simple, flexible and portable. The only downside is speed of execution, as there remains a large performance gap between even efficient interpreters and systems that include a just-in-time (JIT) compiler. Augmenting an interpreter with a JIT, however, is not a small task. Today, Java JITs are typically method-based. To compile whole methods, the JIT must re-implement much functionality already provided by the interpreter, leading to a ``big bang'' development effort before the JIT can be deployed. Addi

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Fu, Guan-cheng, and 傅冠程. "Reducing Dynamic Branch Predictor Table Lookups by Dynamically Collecting Non-branch Instructions." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/29730043936615236092.

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碩士<br>大同大學<br>資訊工程學系(所)<br>97<br>This thesis proposes a new scheme of dynamically collecting non-branch instructions to reduce the accesses of the dynamic branch predictor. Accordingly, the power consumption of the traditional dynamic branch predictor exercised every cycle can be reduced. In the proposed scheme, the branch target buffer, BTB, is modified to record the number of non-branch instructions for the corresponding entry. In the instruction fetch stage, the BTB is read out not only the target address but also the number of upcoming non-branch instructions till encountering the next bra

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Lenahan, Frances D., Thomas M. Koller, Tobias Klein, and Andreas P. Fröba. "Improvement of a transferable force field for the prediction of self-diffusivity, viscosity, surface tension, and density of long-chained linear and branched alkanes and alcohols up to 573 K by molecular dynamics simulations." 2019. https://ul.qucosa.de/id/qucosa%3A72428.

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Books on the topic "Dynamic branch prediction"

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Pierard, Luc A., and Lauro Cortigiani. Stress echocardiography: diagnostic and prognostic values and specific clinical subsets. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198726012.003.0015.

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Stress echocardiography is a widely used method for assessing coronary artery disease, due to its high diagnostic and prognostic value. While inducible ischaemia predicts an unfavourable outcome, its absence is associated with a low risk of future cardiac events. The method provides superior diagnostic and prognostic information than standard exercise electrocardiography and perfusion myocardial imaging in specific clinical subsets, such as women, hypertensive patients, and patients with left bundle branch block. Stress echocardiography allows effective risk assessment also in the diabetic pop

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Book chapters on the topic "Dynamic branch prediction"

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Vajda, Sandor, and Charles Delisi. "An Adaptive Branch-and-Bound Minimization Method Based on Dynamic Programming." In The Protein Folding Problem and Tertiary Structure Prediction. Birkhäuser Boston, 1994. http://dx.doi.org/10.1007/978-1-4684-6831-1_13.

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Mangean, Armel, Jean-Luc Béchennec, Mikaël Briday, and Sébastien Faucou. "WCET Analysis by Model Checking for a Processor with Dynamic Branch Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66176-6_5.

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Simner, Ben, Shaked Flur, Christopher Pulte, et al. "ARMv8-A System Semantics: Instruction Fetch in Relaxed Architectures." In Programming Languages and Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44914-8_23.

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AbstractComputing relies on architecture specifications to decouple hardware and software development. Historically these have been prose documents, with all the problems that entails, but research over the last ten years has developed rigorous and executable-as-test-oracle specifications of mainstream architecture instruction sets and “user-mode” concurrency, clarifying architectures and bringing them into the scope of programming-language semantics and verification. However, the system semantics, of instruction-fetch and cache maintenance, exceptions and interrupts, and address translation,

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Bargagli-Stoffi, Falco J., Jan Niederreiter, and Massimo Riccaboni. "Supervised Learning for the Prediction of Firm Dynamics." In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_2.

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AbstractThanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms’ performance. In this chapter, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The stages we will focus on are (1) startup and innovation, (2) gr

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Naumenko, Maksym, Iryna Hrashchenko, Tetiana Tsalko, Svitlana Nevmerzhytska, Svitlana Krasniuk, and Yurii Kulynych. "Innovative technological modes of data mining and modelling for adaptive project management of food industry competitive enterprises in crisis conditions." In PROJECT MANAGEMENT: INDUSTRY SPECIFICS. TECHNOLOGY CENTER PC, 2024. https://doi.org/10.15587/978-617-8360-03-0.ch2.

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Developed in this research scientific and practical applied project solutions regarding Data Mining for enterprises and companies (on the example of food industry) involve the application of advanced cybernetic computing methods/algorithms, technological modes and scenarios (for integration, pre-processing, machine learning, testing and in-depth comprehensive interpretation of the results) of analysis and analytics of large structured and semi-structured data sets for training high-quality descriptive, predictive and even prescriptive models. The proposed by authors multi-mode adaptive Data Mi

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Zhang, Xudong, Peng Peng, Xuewen Chen, Yulei Wu, Lingdong Shen, and Haina Tang. "STPDN: Spatio-Temporal Pattern Decomposition Network with Fluctuation Awareness for Robust Traffic Flow Forecasting." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240789.

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The significance of traffic prediction in modern urban life has become increasingly prominent. Accurate traffic forecasting improves urban traffic management and enhances road resource utilization. In recent years, many models have introduced spatio-temporal contextual embeddings to distinguish between different time steps and spatial nodes. However, these models often overlook anomalous fluctuations in traffic data due to data imbalance. Consequently, performance declines when encountering uncommon situations, especially those caused by unexpected traffic accidents. To maintain overall perfor

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Saxena, Ankita, Mohammad Irfan, and Rui Dias. "Navigating the Future." In Advances in Human Resources Management and Organizational Development. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6412-3.ch008.

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The dynamic nature of today's workplace presents special opportunities as well as challenges for HRM. Natural Language Processing, a branch of artificial intelligence, has the potential to completely transform human resource management (HRM) by means of the use of data-driven decision-making, bias mitigation, employee experience personalization, as well as procedure optimization. The first section of the paper provides an overview of Natural Language Processing application to HRM, with a particular focus on forward-thinking employee turnover prediction, personalized onboarding and training, re

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Reepu, Sanjay Taneja, and Zelhuda Shamsuddin. "A Machine Learning Algorithm for Forecasting Customer Churn in the Motor Insurance Industry." In Data Alchemy in Insurance: Revolutionizing the Insurance Industry through Big Data Analytics. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815313833125010004.

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In the realm of motor insurance, customer retention has become a critical focus for companies, given the high cost of acquiring new customers. This study proposes a machine learning model to predict customer churn in the motor insurance sector. The objective is to develop a high-accuracy prediction model, identify significant factors for customer attrition, and create customer segments using machine learning algorithms. The literature review highlights the challenges faced by motor insurance providers, such as annual policy renewals and intense competition. Existing research emphasizes the imp

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Kumar, Nitendra, Priyanka Agarwal, Gauri Gupta, Sadhana Tiwari, and Padmesh Tripathi. "AI-Driven Financial Forecasting." In Advances in Business Information Systems and Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1598-9.ch006.

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In today's dynamic and complex financial landscape, accurate and reliable financial forecasting is crucial for businesses to make informed decisions, manage risks, and achieve long-term success. Artificial intelligence (AI) has emerged as a transformative force in the financial industry, offering a powerful set of tools and techniques for enhancing financial forecasting capabilities. This chapter delves into the realm of AI-driven financial forecasting, exploring the role of soft computing techniques in harnessing the power of AI for data-driven financial insights. Soft computing, a branch of

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Davies, T., and J. C. R. Hunt. "New Developments in Numerical Weather Prediction." In Numerical Methods for Fluid Dynamics V. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198514800.003.0001.

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Abstract The essential procedure for numerical weather prediction (NWP), first set out by Richardson in his 1922 book ‘Weather Prediction by Numerical Process’ (26] and similar to that followed in many branches of computational fluid dynamics (CFD), is (a) to divide the atmosphere into flat grid ‘boxes’ (that are shallow near the ground and deep in the stratosphere), (b) using the differential equations and thermodynamics to estimate average values of physical quantities (such as pressure and velocity) within these boxes; and (c) using the equations again, to calculate how these average quanti

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Conference papers on the topic "Dynamic branch prediction"

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Pan, Shien-Tai, Kimming So, and Joseph T. Rahmeh. "Improving the accuracy of dynamic branch prediction using branch correlation." In the fifth international conference. ACM Press, 1992. http://dx.doi.org/10.1145/143365.143490.

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Nayak, Subramanya G. "Dynamic Branch Prediction for Embedded System Applications." In 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces45898.2019.9002301.

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Arora, Harsh, Sagar Kotecha, and Romil Samyal. "Dynamic Branch Prediction Modeller for RISC Architecture." In 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA). IEEE, 2013. http://dx.doi.org/10.1109/icmira.2013.84.

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Lapotre, Vianney, Philippe Coussy, Cyrille Chavet, Hugues Wouafo, and Robin Danilo. "Dynamic branch prediction for high-level synthesis." In 2013 23rd International Conference on Field Programmable Logic and Applications (FPL). IEEE, 2013. http://dx.doi.org/10.1109/fpl.2013.6645540.

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Bharadwaj, Vishnu P., and Manjunath Kohalli. "Dual decode architecture for dynamic branch prediction." In 2017 2nd International Conference for Convergence in Technology (I2CT). IEEE, 2017. http://dx.doi.org/10.1109/i2ct.2017.8226306.

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Tate, Daniel, Gordon Steven, and Paul Findlay. "Investigating the Relative Performance of Static and Dynamic Instruction Scheduling." In International Symposium on Computer Architecture and High Performance Computing. Sociedade Brasileira de Computação, 1999. http://dx.doi.org/10.5753/sbac-pad.1999.19788.

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There are two distinct groups of research into ILP. Those that strongly favour static instruction scheduling and those that favour dynamic instruction scheduling. This paper introduces powerful static and dynamic scheduling models and combines them within the framework of a single simulation environment. Both individual models achieve respectable speedups; dynamic schedullng significantly out-performs static scheduling when an idealised processor model with perfect branch prediction is used. However, when a realistic branch predictor is substituted, the roles are reversed, and static schedulin

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Ghandour, Walid J., and Nadine J. Ghandour. "Leveraging dynamic slicing to enhance indirect branch prediction." In 2014 32nd IEEE International Conference on Computer Design (ICCD). IEEE, 2014. http://dx.doi.org/10.1109/iccd.2014.6974696.

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Jiménez, Daniel A. "Code placement for improving dynamic branch prediction accuracy." In the 2005 ACM SIGPLAN conference. ACM Press, 2005. http://dx.doi.org/10.1145/1065010.1065025.

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Lam, Norman, Si-En Chang, and Mark L. Manwaring. "Evaluating the performance of dynamic branch prediction schemes with BPSim." In the 1997 workshop. ACM Press, 1995. http://dx.doi.org/10.1145/1275165.1275174.

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Gloy, Nicolas, Cliff Young, J. Bradley Chen, and Michael D. Smith. "An analysis of dynamic branch prediction schemes on system workloads." In the 23rd annual international symposium. ACM Press, 1996. http://dx.doi.org/10.1145/232973.232977.

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