Adaptive Branch Prediction Throttling for Low-Confidence Speculation
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Solution Overview
Problem
Modern computer processors face high energy costs due to frequent branch mispredictions and long branch resolution latencies, as existing techniques for limiting speculation do not account for dynamic operating conditions and performance benefits.
Innovation Solution
Adaptive speculation limiting (ASL) dynamically controls speculation based on workload behavior and runtime conditions, using branch confidence data structures and global thresholds to throttle speculation in low-confidence scenarios, reducing energy consumption without sacrificing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speculative execution is used to improve instruction throughput, then single thread performance increases, but energy consumption increases due to pipeline clearing on branch mispredictions
Solution Approach 1:
The system dynamically adjusts the speculation threshold based on runtime conditions including branch misprediction rate, pipeline depth, and workload characteristics. The threshold is not fixed but adapts to changing operational states, allowing the processor to optimize between speculative execution benefits and energy consumption costs in real-time
Solution Approach 2:
The invention changes the parameter of speculation threshold from a static value to a dynamically adjustable parameter. By modifying this parameter based on measured branch misprediction rates and operational conditions, the system can shift between aggressive speculation (high throughput) and conservative speculation (low energy consumption) modes
2Use of energy by moving object
If branch prediction throttling is applied to reduce energy costs, then energy consumption decreases, but performance may be sacrificed due to limited speculation
Solution Approach 1:
The throttling mechanism is dynamic rather than static. The system continuously monitors branch misprediction rates and adjusts the speculation threshold accordingly, enabling the processor to maintain high performance when misprediction rates are low while reducing energy consumption when misprediction rates increase
Solution Approach 2:
The system implements feedback loops that measure actual branch misprediction rates and use this information to adjust the speculation threshold. This closed-loop control ensures that throttling is applied only when necessary, preventing performance degradation while achieving energy savings
3Use of energy by moving object
If prior techniques gate pipeline stages to reduce speculative instructions, then energy costs are reduced, but they fail to account for dynamic operating conditions and performance benefits of wrong path execution
Solution Approach 1:
The invention makes the speculation control mechanism dynamic by continuously monitoring runtime conditions such as branch misprediction rate, pipeline depth, and workload characteristics. This allows the system to adapt to changing operational conditions rather than using fixed gating rules
Solution Approach 2:
The system changes multiple parameters simultaneously including speculation threshold, pipeline gating depth, and threshold adjustment rate based on measured conditions. This multi-parameter adaptation enables the system to respond appropriately to different dynamic operating scenarios
Data Source
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AI summary
Methods and apparatus to implement adaptive branch prediction throttling are disclosed. In one embodiment, the method comprises determining, based on looking up a branch confidence data structure, whether a current branch in execution of a single thread is a low confidence branch in which a branch predictor of a processor has a low level of certainty that outcome of the current branch is predicted correctly; and comparing a branch misprediction rate, a microinstruction waste rate, and a cache missing rate of the single thread with their corresponding thresholds. The method further comprises throttling branch prediction of the current branch based on the determination of the current branch being a low confidence branch and one or more thresholds for the branch misprediction rate, the microinstructions waste rate, and the cache missing rate of the single thread being crossed.