Load Value Prediction Training and Table Fusion
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Solution Overview
Problem
Processing systems face challenges in improving throughput and reducing power consumption, particularly due to load execution latency, which can account for 20% to 40% of executed instructions and leads to processor stalls and increased power usage.
Innovation Solution
The implementation of load value predictors, including last value predictors, stride address predictors, content address predictors, and content value predictors, which train to predict load values or addresses, allowing speculative execution and reducing latency through accurate predictions and recovery actions, along with smart training and table fusion optimizations to enhance predictor efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple load value predictors are trained simultaneously, then prediction accuracy improves, but power consumption increases and training efficiency decreases
Solution Approach 1:
The system employs self-service through automatic predictor selection and training management. The load value prediction unit automatically determines which predictors to train based on prediction outcomes, and the system self-regulates training frequency and resource allocation without external intervention, optimizing power usage while maintaining accuracy
Solution Approach 2:
The system dynamically changes training parameters including training frequency, predictor activation states, and resource allocation based on runtime performance metrics. This allows the system to adapt power consumption levels while maintaining prediction accuracy by adjusting training intensity according to actual needs
2Measurement precision
If load value predictors are continuously trained, then prediction accuracy improves, but processor performance decreases due to training overhead
Solution Approach 1:
The system implements periodic action by training predictors at specific intervals rather than continuously. Training is triggered by events such as misprediction detection or after a predetermined number of predictions, allowing normal processor operations to proceed uninterrupted while still maintaining prediction accuracy through regular updates
Solution Approach 2:
The system performs preliminary action by pre-training predictors during idle periods or before predicted high-load scenarios. This allows the predictors to be ready with accurate models before actually needed, avoiding training overhead during critical execution paths
3Adaptability or versatility
If predictor tables are expanded to handle more predictions, then prediction coverage improves, but hardware complexity and area increase
Solution Approach 1:
The system applies segmentation by dividing predictor tables into multiple smaller, specialized tables or partitions. Each table handles specific types of predictions or address spaces, reducing the complexity of any single table while collectively providing comprehensive coverage. This modular approach allows independent optimization of each segment
Solution Approach 2:
The system implements universality by designing predictor tables that can serve multiple purposes and handle diverse prediction scenarios. The same table structure and hardware resources are used across different prediction contexts, eliminating the need for separate dedicated tables for each prediction type and reducing overall hardware complexity
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for training load value predictors, comprising: determining if a prediction has been made by one or more of a plurality of load value predictors; determining a misprediction has been made by one or more load value predictors of the plurality of load value predictors; training each of the one or more load value predictors that made the misprediction; and resetting a confidence value associated with each of the one or more load value predictors that made the misprediction.