Comparative Analysis of Branch Prediction Techniques Across Diverse Benchmark Suites

Author(s)	Sai Kumar Marri, E. Sikender
Country	India
Abstract	Branch prediction is a critical aspect of modern microprocessor design, significantly influencing performance and energy efficiency in pipelined architectures. Accurate branch predictors reduce pipeline stalls, enhance instruction-level parallelism and overall system throughput. This study provides a comprehensive analysis of various branch prediction techniques, including bimodal predictors, perceptron-based predictors, hybrid schemes, and low-power alternatives, as applied to diverse benchmark suites such as SPEC CPU2000, Mibench, and Mediabench. The paper explores the architectural principles, advantages, and limitations of these predictors, emphasizing their accuracy, power consumption, and hardware overhead. Key innovations like genetic algorithm-enhanced predictors and neural network-based designs are discussed, highlighting their ability to adapt dynamically to workload characteristics. Furthermore, the study examines novel approaches, such as undervolting predictors for energy efficiency and complementary predictors designed to address misprediction patterns.
Keywords	Branch Prediction, Processor Performance, Energy Efficiency, Benchmark Analysis, Hybrid Predictors.
Field	Engineering
Published In	Volume 3, Issue 4, July-August 2021
Published On	2021-08-25
DOI	https://doi.org/10.36948/ijfmr.2021.v03i04.33084
Short DOI	https://doi.org/g82j6b

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International Journal For Multidisciplinary Research