Branch Predictor Escape Mechanism for Microprocessor Cache
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Solution Overview
Problem
Conventional branch prediction schemes in microprocessors face inefficiencies when handling multiple branch instructions in a single cache line fetch, particularly when a more complex branch instruction is allocated to a side with lower prediction complexity, leading to increased misprediction rates and performance degradation.
Innovation Solution
A microprocessor architecture with three cache memory sides, utilizing two types of conditional branch predictors of different complexities, where a mispredicted conditional branch can be moved from a lower complexity side to a higher complexity side based on a configurable probability, reducing misprediction recurrence and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-sided BTAC structure is used, then the device complexity is reduced, but the ability to handle multiple branch instructions in a cache line fetch is insufficient
Solution Approach 1:
The BTAC is divided into multiple sides (first side, second side, third side), each capable of storing branch instruction information. This segmentation allows the structure to handle multiple branch instructions simultaneously while maintaining manageable complexity through modular organization.
Solution Approach 2:
Each side of the BTAC is designed with universal functionality to store and predict branch instructions. The prediction tables on each side can handle different branch instructions using the same basic structure, enabling the system to process multiple branch types without requiring entirely separate mechanisms for each.
2Device complexity
If all sides use the same complexity predictor, then the device complexity is minimized, but the prediction accuracy for complex branch instructions deteriorates
Solution Approach 1:
Different sides of the BTAC are assigned different predictor complexities based on local requirements. Sides experiencing complex branch patterns use more sophisticated predictors, while sides with simpler patterns use lighter predictors. This local differentiation optimizes overall prediction accuracy without uniformly increasing complexity across all sides.
Solution Approach 2:
The system dynamically selects which side to use for a given branch instruction based on the branch's characteristics and historical performance. This dynamic allocation allows complex branches to be routed to sophisticated predictors while simple branches use efficient simple predictors, optimizing the balance between complexity and accuracy.
3Device complexity
If complex branch instructions are handled by simple predictors, then the device complexity is reduced, but the misprediction rate increases
Solution Approach 1:
The system incorporates feedback mechanisms that monitor prediction accuracy for each side and branch type. When mispredictions are detected, the system can adjust which side handles future branch instructions or trigger updates to prediction tables. This feedback loop ensures that complex branches are progressively routed to more capable predictors, reducing the overall misprediction rate.
Solution Approach 2:
The BTAC sides perform self-evaluation of their prediction performance and can autonomously determine when to take over handling of specific branch instructions. This self-service capability allows the system to automatically optimize the allocation of branch handling without external intervention, reducing mispredictions by ensuring complex branches are handled by appropriately complex predictors.
4Reliability
If multiple complex predictors are used on all sides, then the prediction accuracy is improved, but the device complexity and resource usage increase
Solution Approach 1:
The system changes the complexity parameter of predictors based on the specific needs of different branch instructions and the performance characteristics of different sides. Rather than using maximum complexity uniformly, the system adjusts predictor complexity parameters dynamically, allocating higher complexity only where needed to maintain accuracy while controlling overall system complexity.
Solution Approach 2:
The system applies complex prediction mechanisms partially, only to the extent necessary for achieving acceptable accuracy. Simple predictors are used wherever possible, with complex predictors activated only for specific complex branch patterns. This partial application of complexity achieves sufficient prediction accuracy without the full overhead of universal complex prediction.
Data Source
AI summary
In one embodiment, a branch prediction control system is configured to move a mispredicted conditional branch from a smaller cache side that uses the lower complexity conditional branch predictor to one of the two large cache sides that uses the higher complexity conditional branch predictors. The move (write) is achieved according to a configurable probability or chance to escape misprediction recurrence and results in a reduced amount of mispredictions for the given branch instruction.