Pipeline Prediction State Updates to Reduce Divergence Bubbles
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Solution Overview
Problem
Pipelined prediction algorithms in data processing apparatuses face performance and power consumption issues due to pipeline bubbles caused by frequent divergences between preliminary and subsequent predictions, leading to inefficient processing and increased energy consumption.
Innovation Solution
Implement divergence-triggered updates to the prediction state data used for preliminary predictions when a divergence is detected, applying the update conditionally based on a probabilistic test to minimize the frequency of pipeline bubbles and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pipelined prediction algorithms are used to generate preliminary predictions quickly, then processing speed is improved, but prediction accuracy deteriorates due to frequent divergences between preliminary and subsequent predictions
Solution Approach 1:
The patent applies preliminary action by updating the prediction state data in advance when a divergence is detected between preliminary and subsequent predictions. This proactive update occurs before the next prediction cycle, ensuring that the prediction state data is already optimized when subsequent predictions are generated, thereby reducing future divergences while maintaining current processing speed.
2Measurement precision
If prediction state data is updated frequently to improve prediction accuracy, then prediction accuracy is improved, but power consumption increases due to additional update operations
Solution Approach 1:
The patent implements feedback by monitoring prediction divergences and using this information to trigger selective updates of the prediction state data. The update mechanism is activated only when a divergence occurs between preliminary and subsequent predictions, creating a feedback loop that adjusts prediction state data based on actual prediction performance, thereby improving accuracy while minimizing unnecessary updates and power consumption.
3Measurement precision
If divergence-triggered updates are applied to prediction state data, then prediction accuracy is improved, but pipeline complexity increases due to additional update logic
Solution Approach 1:
The patent applies the taking out principle by extracting the update logic into a separate, dedicated module that handles prediction state data updates independently from the main prediction pipeline. This modular approach isolates the complexity of divergence detection and state updates, allowing the core prediction logic to remain simple while adding update capabilities through a specialized component that operates based on divergence signals.
Data Source
AI summary
An apparatus is provided comprising prediction state storage circuitry to maintain a set of prediction state data and prediction circuitry configured to generate predictions in pipeline stages. The prediction circuitry is configured to, in a preliminary pipeline stage, generate a preliminary prediction depending on a subset of the prediction state data for use by at least one other component, and in a subsequent pipeline stage, to generate a subsequent prediction depending on the set of the prediction state data. The apparatus further comprises overriding circuitry responsive to a determination that the preliminary prediction and subsequent prediction are different to cause the at least one other component to use the subsequent prediction instead of the preliminary prediction and state update circuitry configured to apply, in response to detecting a divergence-triggered update condition being satisfied, a divergence-triggered update to the subset of the prediction state data.