Resource-Adaptive Prediction Circuitry for Speculative Actions
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Solution Overview
Problem
Existing prediction mechanisms face issues of overconfidence or underconfidence due to long training times, exacerbated by changing system resource availability, leading to inefficient use of system resources.
Innovation Solution
The prediction circuitry varies action selection criteria based on current system resource conditions, adjusting thresholds and lookahead depths to balance performance and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prediction state information is trained using previous events during instruction processing, then prediction accuracy improves, but training time increases causing delays
Solution Approach 1:
The system performs preliminary training of prediction state information in the background during instruction processing, preparing predictions in advance without blocking the main processing flow. This allows the prediction mechanism to have accurate predictions ready before they are needed, reducing the time penalty associated with training.
Solution Approach 2:
The system dynamically adjusts the training process by varying action selection criteria based on current system resource conditions. When resources are abundant, more training can be performed; when resources are constrained, training is reduced. This dynamic adaptation balances prediction accuracy with time loss.
2Productivity
If speculative actions are initiated based on predictions before certainty is known, then performance improves by reducing delays, but system resource utilization may deteriorate due to potential mispredictions
Solution Approach 1:
The system changes parameters of the prediction mechanism by varying action selection criteria based on current system resource conditions. When system resources are abundant, the system can afford to initiate more speculative actions with lower confidence thresholds. When resources are constrained, the system raises confidence requirements, reducing speculative actions and thus reducing resource waste from mispredictions.
Solution Approach 2:
The system uses feedback from system resource conditions to adjust prediction behavior. By monitoring current resource availability and feeding this information back to the prediction circuitry, the system can adaptively control the aggressiveness of speculative actions, balancing performance gains against resource consumption.
3Stability of the object's composition
If fixed action selection criteria are used for speculative actions, then system stability is maintained, but adaptability to changing resource conditions deteriorates
Solution Approach 1:
The system transitions from static to dynamic action selection criteria by making them variable based on current system resource conditions. This allows the prediction mechanism to maintain stability through systematic decision-making while simultaneously adapting to changing resource availability, resolving the contradiction between stability and adaptability.
Data Source
AI summary
An apparatus comprises processing circuitry to perform data processing in response to instructions; prediction state storage circuitry to store prediction state information; prediction state training circuitry to train the prediction state information in response to events detected during processing of instructions by the processing circuitry; and prediction circuitry to predict, based on the prediction state information, a given speculative action to be performed in response to a given prediction trigger event; in which: the prediction circuitry varies, based on one or more current system resource conditions of the apparatus, at least one action selection criterion used to select which speculative action is to be performed.