Confidence-Gated Multi-Taken Control Flow Prediction
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Solution Overview
Problem
Existing data processing systems struggle with predicting control flow changes efficiently, leading to mispredictions that hinder processor performance, particularly when dealing with multi-taken sequences involving conditional control flow changing instructions.
Innovation Solution
An apparatus with prediction circuitry that predicts multi-taken sequences, calculating confidence levels for these sequences and allowing predictions only when the confidence level meets a certain condition, thereby improving prediction bandwidth while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prediction circuitry attempts to predict all control flow sequences including multi-taken sequences, then prediction bandwidth is improved, but misprediction rate increases
Solution Approach 1:
The patent introduces confidence levels as a parameter to evaluate the reliability of predicted multi-taken sequences. The prediction circuitry calculates confidence levels based on historical execution data and only allows prediction when the confidence level exceeds a threshold, thus changing the prediction behavior based on parameter evaluation rather than attempting to predict all sequences.
Solution Approach 2:
The system implements feedback mechanisms where actual execution outcomes of multi-taken sequences are fed back to update confidence levels. This feedback loop allows the prediction circuitry to learn from past predictions and adjust its behavior, improving reliability while maintaining high bandwidth by only predicting sequences with sufficient confidence.
2Adaptability or versatility
If prediction circuitry predicts multi-taken sequences with low confidence, then prediction coverage is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent uses confidence levels as a dynamic parameter to gate prediction behavior. By adjusting the confidence threshold, the system can adapt between covering more sequences (lower threshold) or maintaining higher accuracy (higher threshold), thus resolving the contradiction between coverage and accuracy through parameter control.
3Measurement precision
If prediction circuitry implements confidence level calculation and validation, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary confidence level calculation and validation before actual prediction occurs. By pre-evaluating the confidence of potential multi-taken sequences and storing this information, the prediction circuitry can make accurate predictions without repeatedly performing complex calculations, thus reducing operational complexity while maintaining accuracy.
Data Source
AI summary
An apparatus has a fetch queue to identify a sequence of instructions to be fetched for execution and prediction circuitry to predict upcoming control flow and to control which instructions are identified in the fetch queue in dependence on the prediction. The prediction circuitry predicts multi-taken sequences which are sequences of instructions in which control flow is diverted by a first control flow changing instruction to a series of instructions terminating in a second control flow changing instruction that diverts control flow to a target address. The apparatus also has prediction confidence calculation circuitry to calculate confidence levels for respective multi-taken sequences. Each confidence level is indicative of a confidence in an accuracy of prediction of its respective multi-taken sequence. When the confidence level for a particular multi-taken sequence satisfies a prediction confidence condition, the prediction confidence tracking circuitry allows the particular multi-taken sequence to be predicted by the prediction circuitry. The prediction circuitry causes the series of instructions and the target instruction for the particular multi-taken sequence to be identified in the fetch queue when the prediction circuitry predicts the particular multi-taken sequence and further predictions to be made starting from the target address for the particular multi-taken sequence.