Branch Prediction Latency Reduction via Surprise-Guessed-Branch Detection
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Solution Overview
Problem
Deep pipelining in processors leads to significant performance losses due to branch prediction latency, where incorrect predictions cause wasted delay and reduced pipeline speed, as existing methods either stall instruction fetch or restart branch prediction logic, both of which are detrimental to performance.
Innovation Solution
The method involves identifying surprise-guessed-taken (SGT) and surprise-guessed-not-taken (SGNT) branches and generating signals to block refetch signals, allowing instruction delivery to continue along the predicted path, thereby avoiding unnecessary restarts of branch prediction logic and reducing latency penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If branch prediction logic is restarted upon misprediction, then prediction accuracy is improved, but pipeline latency increases
Solution Approach 1:
The patent implements a start-up counter that预先 (in advance) detects when branch prediction logic needs to be restarted and initiates the restart process before misprediction actually occurs. This preliminary detection and initiation of restart actions reduces the overall latency by avoiding delayed responses to mispredictions.
Solution Approach 2:
The patent dynamically adjusts the behavior of the branch prediction system by conditionally restarting prediction logic based on detected mispredictions and start-up conditions. The system transitions between different operational states (normal operation vs. restart) based on runtime conditions, optimizing the balance between accuracy and latency.
2Reliability
If instruction fetch is stalled to allow prediction to catch up, then prediction accuracy is improved, but instruction fetch performance deteriorates
Solution Approach 1:
The patent applies partial stalling by only halting instruction fetch when absolutely necessary (when start-up conditions are met and misprediction is detected). In normal operation, instruction fetch continues without stalling, maintaining high productivity. The stalling action is applied selectively and minimally to achieve the necessary prediction accuracy.
3Speed
If deeper pipelines are used to increase instruction-level parallelism, then processing speed is improved, but susceptibility to performance loss from branch mispredictions increases
Solution Approach 1:
The patent implements a feedback mechanism where mispredictions are detected and fed back to the branch prediction logic, triggering a restart of the prediction system. This closed-loop feedback ensures that deeper pipelines maintain high accuracy by automatically correcting prediction errors, thereby reducing performance loss from mispredictions while preserving the speed benefits of deep pipelining.
Data Source
AI summary
A branch prediction algorithm is used to generate a prediction of whether or not a branch will be taken. One or more instructions are fetched such that, for each of the fetched instructions, the prediction initiates a fetch of an instruction at a predicted target of the branch. A test is performed to ascertain whether or not the prediction was generated late relative to the fetched instructions, so that if the branch is later detected as mispredicted, that detection can be correlated to the late prediction. When the prediction is generated late relative to the fetched instructions, a latent prediction is selected by utilizing a fetching initiated by the latent prediction such that a new fetch is not started.