Branch Prediction Correction Using Selective Predictor Power-Up
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Solution Overview
Problem
Branch misprediction in processors leads to increased instruction fetch latency and power dissipation, necessitating improved branch prediction accuracy and reduced power consumption.
Innovation Solution
A branch prediction unit selectively powers up a subset of prediction structures to obtain branch prediction information, dynamically adjusting power states based on the relevance of these structures to the conditional branch, and repredicts if necessary to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the branch prediction unit accesses all prediction structures to ensure complete branch prediction information, then branch prediction accuracy is improved, but power consumption increases
Solution Approach 1:
The prediction structures are divided into multiple separate structures (e.g., local prediction structure and global prediction structure), each storing different types of branch prediction information. The branch prediction unit selectively accesses only the relevant structures based on the specific branch instruction characteristics, avoiding the need to power up and access all structures simultaneously, thus reducing power consumption while maintaining accuracy.
Solution Approach 2:
Different prediction structures are designed with specialized characteristics suited to specific types of branch instructions. The system determines which structure(s) are relevant for a given branch instruction and accesses only those, optimizing the balance between prediction accuracy for the specific case and power consumption avoidance.
2Reliability
If the branch prediction unit powers up all prediction structures, then complete branch prediction information is available, but instruction fetch latency increases due to power-up time
Solution Approach 1:
By segmenting prediction structures into separate accessible units, the system can power up only the necessary structures for current branch prediction needs rather than all structures. This reduces the time required to make prediction information available, decreasing instruction fetch latency while maintaining the ability to provide accurate predictions when needed.
Solution Approach 2:
The system maintains prediction structures in a powered-down state until needed, then selectively powers up only the relevant structures for the current branch prediction task. This preliminary state management avoids unnecessary power-up delays while ensuring complete information is available when required.
3Reliability
If the branch prediction unit uses multiple prediction structures, then prediction accuracy for complex branches is improved, but device complexity increases
Solution Approach 1:
The complex prediction system is segmented into multiple independent prediction structures, each handling specific aspects of branch prediction. This modular approach makes the complex system more manageable and easier to implement, as each structure can be designed and optimized independently while working together to provide comprehensive prediction accuracy.
Solution Approach 2:
The system dynamically selects which prediction structures to access based on the characteristics of the branch instruction being processed. This dynamic approach allows the system to utilize multiple structures for complex branches when needed while avoiding the complexity of accessing all structures for simple branches, effectively managing device complexity.
Data Source
AI summary
A branch prediction unit of the processor powers-up and accesses only a subset of a plurality of prediction structures to obtain a first set of branch prediction information for a conditional branch. During the access, at least one of the plurality of prediction structures remains powered-down. The branch prediction unit thereafter determines whether all of the plurality of prediction structures having branch prediction information relevant to the conditional branch were accessed. Based on a determination that fewer than all of the plurality of prediction structures having branch prediction information relevant to the conditional branch were accessed, the branch prediction unit refrains from outputting a branch prediction based on the first set of branch prediction information, powers-up and accesses a greater number of the plurality of prediction structures to obtain a second set of branch prediction information, and outputs a branch prediction based on the second set of branch prediction information.