Branch Target Buffer MRU Column Prediction Latency
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Solution Overview
Problem
Current branch prediction techniques in processor pipelines face latency issues due to the need for accessing and searching large Branch Target Buffers (BTBs), which can lead to increased prediction delay and reduced performance, even with hierarchical and cooperative predictors.
Innovation Solution
Implementing a method that utilizes most-recently-used (MRU) information from a MRU table associated with the BTB to accelerate branch prediction by speculating that predictions are in the most recently used column, thereby reducing latency and allowing faster reporting of predicted branch information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large Branch Target Buffer is used to store information about more branch instructions, then prediction accuracy is improved, but access latency increases
Solution Approach 1:
The patent segments the Branch Target Buffer into multiple columns (e.g., first column, second column, third column) within each row. This segmentation allows the buffer to store multiple branch predictions in a compact format, improving accuracy without proportionally increasing access latency. The segmented structure enables parallel or selective access to specific columns based on prediction needs.
Solution Approach 2:
The patent pre-organizes branch target information into a structured buffer format with multiple columns before access is needed. By maintaining this pre-organized structure with preliminary sorting or arrangement of branch predictions across columns, the system reduces the time required to retrieve predictions during actual branch execution, as the data is already positioned for efficient access.
2Measurement precision
If hierarchical predictors and cooperative predictors are implemented, then branch prediction capability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple branch prediction functionalities into a single unified Branch Target Buffer structure. Instead of implementing separate hierarchical predictors and cooperative predictors as distinct complex structures, the invention combines their capabilities into one integrated buffer that handles both local and global prediction needs, reducing overall device complexity while maintaining prediction capability.
Solution Approach 2:
The Branch Target Buffer is designed with multi-functionality to serve multiple prediction purposes simultaneously. The same buffer structure supports both hierarchical prediction (for local patterns) and cooperative prediction (for global patterns) without requiring separate dedicated structures, thereby improving branch prediction capability across different scenarios while avoiding the complexity overhead of multiple specialized predictors.
Data Source
AI summary
Exemplary embodiments include a system and method for reducing branch prediction latency using a branch target buffer with most recently used column prediction. An exemplary embodiment includes a method for reducing branch prediction latency, the method including reading most-recently-used information from a most-recently-used table associated with the branch target buffer where each most-recently-used entry corresponds to one or more branch target buffer rows and specifies the ordering from least-recently-used to most-recently-used of the associated branch target buffer columns, selecting a row from the branch target buffer and simultaneously selecting the associated entry from the most-recently-used table and speculating that there is a prediction in the most recently used column of the plurality of columns from the selected row from the branch target buffer while determining whether there is a prediction and which column contains the prediction.