Load Value Prediction Circuitry for Temporal Sequences
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Solution Overview
Problem
Existing load operation prediction schemes struggle to effectively predict temporal sequences of load values, where the periodicity of the sequence differs from the identifiers associated with the load instructions, leading to inefficiencies in data processing.
Innovation Solution
The apparatus comprises processing circuitry to execute load operations and prediction circuitry that receives a given load value to make predictions for subsequent load operations. The prediction circuitry determines a predicted load value and an ID-delta value, which is the difference between the identifier of the given load operation and the identifier of the subsequent load operation, based on observed patterns in previously-executed sequences of load operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional load value prediction schemes are used, then prediction can be made for regular sequences, but they fail to accurately predict temporal sequences where periodicity differs from identifier patterns
Solution Approach 1:
The prediction scheme dynamically adapts by detecting temporal patterns in load values independently of identifier sequences. The system transitions from static identifier-based prediction to dynamic value-based pattern recognition, allowing accurate prediction of temporal sequences where the periodicity of load values differs from the periodicity of load instruction identifiers.
Solution Approach 2:
The invention changes the prediction parameter from identifier-based patterns to load value-based temporal patterns. By monitoring the actual load values returned and detecting their temporal periodicity, the system can predict future load values even when the identifier sequence periodicity does not match the value sequence periodicity, thus improving prediction accuracy for diverse sequence patterns.
2Reliability
If load operations are executed with full latency, then accurate data values are obtained, but performance is degraded due to long waiting times
Solution Approach 1:
The prediction circuitry performs preliminary action by predicting future load values before the actual load operations complete. When a temporal pattern is detected in previously returned load values, the system can provide predicted values to dependent instructions in advance, allowing processing to continue without waiting for the full memory latency, thus improving throughput while maintaining data accuracy through pattern-based prediction.
Solution Approach 2:
The system uses feedback from previously executed load operations to improve future predictions. By monitoring the temporal patterns in load values returned from memory and using this feedback to refine prediction accuracy, the system can reliably predict future values and reduce latency impact on performance while maintaining data accuracy through continuous pattern validation.
Data Source
AI summary
Processing circuitry to execute load operations, each associated with an identifier. Prediction circuitry to receive a given load value associated with a given identifier, and to make, in dependence on the given load value, a prediction indicating a predicted load value for a subsequent load operation to be executed by the processing circuitry and an ID-delta value indicating a difference between the given identifier and an identifier of the subsequent load operation. The predicted load value being predicted in dependence on at least one occurrence of each of the given load value and the predicted load value during execution of a previously-executed sequence of load operations. The prediction circuitry is configured to determine the ID-delta value in dependence on a difference between identifiers associated with the at least one occurrence of each of the given load value and the predicted load value in the previously-executed sequence of load operations.