Dynamic Value Prediction Allocation for Pipeline Interlocks
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Solution Overview
Problem
In data processing, dependencies between instructions can lead to inefficient pipeline usage due to interlocks, resulting in pipeline bubbles and reduced throughput, as subsequent instructions are delayed until the result of earlier instructions is available.
Innovation Solution
A data processing apparatus with value prediction storage circuitry that speculatively issues dependent instructions using predicted values, and a dynamic allocation policy that prioritizes storing result values from instructions that create interlocks, especially when associated with empty stages in the pipeline, to reduce pipeline bubbles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the value predictor stores data value predictions for all executed instructions, then the accuracy of value predictions improves, but the storage capacity is exceeded and the complexity of managing the predictor increases
Solution Approach 1:
The patent applies local quality by differentiating between regular instructions and interlock-causing instructions. Instead of treating all instructions uniformly, the system selectively allocates value predictions based on the specific characteristics of instructions that create interlocks. This targeted approach concentrates storage resources on the most critical instructions, improving prediction accuracy for bottleneck-causing operations without proportionally increasing overall storage requirements or management complexity.
2Productivity
If the value predictor uses a static allocation policy, then the simplicity of the allocation mechanism is maintained, but the efficiency of reducing pipeline bubbles is reduced
Solution Approach 1:
The patent implements dynamics by transitioning from a static allocation policy to a dynamic one. The allocation mechanism now adapts its behavior based on real-time pipeline conditions, specifically detecting when interlocks occur and prioritizing value prediction allocation for instructions that cause these interlocks. This dynamic adjustment allows the system to optimize pipeline throughput by focusing resources on critical paths, while the complexity increase is localized to the allocation logic that responds to interlock detection events.
3Loss of time
If speculative instruction issuance is performed frequently, then the reduction in pipeline hold-ups improves, but the penalty from correcting prediction errors increases
Solution Approach 1:
The patent applies parameter changes by modifying the allocation criteria for value predictions based on interlock detection. Instead of uniformly enabling speculative issuance for all instructions, the system changes the parameter of allocation probability - increasing it for interlock-causing instructions and maintaining or reducing it for others. This selective parameter adjustment allows aggressive speculation where it provides the most benefit (reducing pipeline hold-ups) while maintaining more conservative approaches elsewhere, thereby managing the overall error correction penalty.
Data Source
AI summary
Apparatuses and methods of data processing are disclosed for processing circuitry having a pipeline of multiple stages. Value prediction storage circuitry holds value predictions, each associated with an instruction identifier. The value prediction storage circuitry performs look-ups and provides the processing circuitry with data value predictions. The processing circuitry speculatively issues a subsequent instruction into the pipeline by provisionally assuming that execution of a primary instruction will result in the generated data value prediction. Allocation of entries into the value prediction storage circuitry is based on a dynamic allocation policy, whereby likelihood of allocation into the value prediction storage circuitry of an data value prediction increases for an executed instruction when the executed instruction is associated with at least one empty processing stage in the pipeline.