Register-Dependent Identifier Generation for Prediction Storage
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Solution Overview
Problem
Existing prediction apparatuses face inefficiencies in training due to inadequate identifier value generation methods, particularly in handling loop unrolling and address distance-based schemes, leading to inefficient use of prediction storage and reduced accuracy.
Innovation Solution
An apparatus with an input interface for receiving training events, using identifier value generation circuitry to generate identifier values based on at least one register referenced by a program instruction, and matching circuitry to update training data in prediction storage, optimizing the allocation and use of entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If address distance-based schemes are used for identifier value generation, then the prediction apparatus can handle loop unrolling, but duplicate entries are created leading to inefficient use of prediction storage
Solution Approach 1:
The patent changes the parameter used for identifier generation from address-distance-based values to register-dependent values. This parameter change allows the system to maintain adaptability to loop unrolling while eliminating duplicate entry creation, as register values provide a more stable and unique identification mechanism across loop iterations.
Solution Approach 2:
The patent uses register values as a copy or representation of the instruction's operational context rather than relying on address distances. This copying approach creates a more accurate fingerprint of the instruction's behavior, preventing duplicate entries while maintaining loop unrolling handling capability.
2Productivity
If traditional identifier value generation is used, then the training process is simpler, but the accuracy of predictions is reduced
Solution Approach 1:
The patent introduces register-dependent identifier values as an intermediary between the training event and the prediction storage entries. This intermediary provides more discriminating information than traditional methods, improving prediction accuracy while maintaining training process efficiency through automated identifier generation.
Solution Approach 2:
The patent changes the identification parameter from coarse address-distance metrics to finer-grained register-dependent values. This parameter refinement improves prediction accuracy by better distinguishing between different training events while the automated generation process maintains training efficiency.
3Measurement precision
If more prediction storage entries are created to handle diverse training events, then prediction accuracy improves, but the device complexity increases
Solution Approach 1:
The patent changes the identifier generation parameter to register-dependent values, which naturally provides better discrimination among training events. This reduces the need for additional storage entries to achieve the same level of prediction accuracy, thereby reducing device complexity while maintaining or improving prediction precision.
Data Source
AI summary
A technique is provided for training a prediction apparatus. The apparatus has an input interface for receiving a sequence of training events indicative of program instructions, and identifier value generation circuitry for performing an identifier value generation function to generate, for a given training event received at the input interface, an identifier value for that given training event. The identifier value generation function is arranged such that the generated identifier value is dependent on at least one register referenced by a program instruction indicated by that given training event. Prediction storage is provided with a plurality of training entries, where each training entry is allocated an identifier value as generated by the identifier value generation function, and is used to maintain training data derived from training events having that allocated identifier value. Matching circuitry is then responsive to the given training event to detect whether the prediction storage has a matching training entry (i.e. an entry whose allocated identifier value matches the identifier value for the given training event). If so, it causes the training data in the matching training entry to be updated in dependence on the given training event.