Dynamic Confidence Threshold for Microprocessor Load Value Prediction
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Solution Overview
Problem
Current microprocessor technologies face performance limitations due to stalls caused by pending load instructions, as data retrieved by load operations are needed by subsequent instructions before completion, leading to time delays even with fast local caches, and existing prediction methods often provide inaccurate data values while consuming significant resources.
Innovation Solution
A method and system that predicts data values for load operations by accessing a load value prediction table, providing predicted values without waiting for load operation completion, using a confidence parameter compared to a dynamic threshold to ensure accuracy and reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data values are predicted using stored load values from previous operations, then prediction accuracy may improve, but system resources are significantly consumed
Solution Approach 1:
The patent applies local quality by selectively replacing only specific table entries in the load value prediction table based on collision parameters, rather than uniformly managing all entries. This targeted approach optimizes resource usage by focusing updates on relevant entries while conserving resources for the overall system.
Solution Approach 2:
The patent changes parameters by introducing collision parameters and confidence parameters that dynamically adjust the prediction process. These parameter changes enable the system to balance prediction accuracy with resource consumption by adapting the prediction behavior based on observed collisions and confidence levels.
2Speed
If speculative load operations are executed before actual load completion, then processing speed improves, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-loading data values into the prediction table during previous load operations. This allows speculative execution to proceed faster by having prediction data prepared in advance, while the confidence parameter mechanism ensures accuracy by validating predictions against actual outcomes.
Solution Approach 2:
The patent uses feedback by comparing predicted data values with actual loaded values and adjusting confidence parameters accordingly. This feedback loop maintains prediction accuracy by learning from past prediction outcomes and adapting the confidence levels for future speculative operations.
3Productivity
If load operations are executed in superscalar design with multiple instructions stalled, then throughput increases, but the load-to-use penalty increases
Solution Approach 1:
The patent applies preliminary action by preparing prediction table entries in advance during previous load operations. This allows superscalar designs to execute multiple instructions in parallel without stalling, as the prediction data is already available, thereby reducing the load-to-use penalty while maintaining high throughput.
Solution Approach 2:
The patent introduces the load value prediction table as an intermediary between memory and the execution units. This intermediary structure allows superscalar pipelines to continue executing multiple instructions by providing predicted values, thus reducing stalls and improving throughput without incurring the full load-to-use penalty.
Data Source
AI summary
Prediction of data values to be read from memory by a microprocessor for load operations. In one aspect, a method for predicting a data value that will result from a load operation to be executed by the microprocessor includes accessing an entry in a load value prediction table that stores a predicted data value corresponding to the load operation. The predicted data value is provided as a result of the load operation without waiting for execution of the load operation to complete based on a confidence parameter stored in the entry compared to a dynamic confidence threshold.