Prefetcher Engine Configuration via Supervised Learning
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Solution Overview
Problem
Existing prefetcher systems face challenges in balancing aggressiveness to optimize memory access performance without causing excessive inter-core interference or high power consumption.
Innovation Solution
A system that uses machine learning to select optimal prefetcher engine configurations by training configuration-specific regression models based on device performance measurements during benchmark applications, allowing for dynamic adjustment of aggressiveness levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If aggressive prefetching is used to improve memory access performance, then data access speed is improved, but inter-core interference increases
Solution Approach 1:
The system dynamically adjusts prefetcher aggressiveness levels based on real-time monitoring of inter-core interference metrics and memory access patterns. The controller continuously adapts the prefetching intensity to match current system conditions, transitioning between conservative and aggressive modes as needed to maintain optimal performance while minimizing interference.
Solution Approach 2:
The system implements feedback mechanisms that monitor memory queue occupancy, cache hit rates, and inter-core interference levels. This feedback is used to adjust prefetcher aggressiveness in real-time, creating a closed-loop control system that balances performance optimization with interference mitigation.
2Loss of time
If aggressive prefetching is used to improve memory access performance, then latency is reduced, but power consumption increases
Solution Approach 1:
The system dynamically adjusts prefetcher operation intensity based on power budget constraints and performance requirements. When power consumption thresholds are exceeded, the controller reduces prefetcher aggressiveness, thereby lowering power usage while maintaining acceptable performance levels.
Solution Approach 2:
The system changes operational parameters of the prefetcher, specifically the aggressiveness level, to balance between latency reduction and power consumption. By adjusting parameters such as prefetch distance and prefetch frequency, the system optimizes the trade-off between performance improvement and energy usage.
3Object-generated harmful factors
If conservative prefetching is used to reduce interference, then inter-core interference is minimized, but memory access performance deteriorates
Solution Approach 1:
The system dynamically transitions between conservative and aggressive prefetching modes based on real-time system conditions. When interference levels are low and performance benefits are high, the system adopts aggressive prefetching. When interference becomes problematic or performance gains diminish, it switches to conservative modes.
Solution Approach 2:
The system uses feedback from performance monitoring to adjust prefetcher aggressiveness. When conservative prefetching is detected to be limiting performance, the system increases aggressiveness. When aggressive prefetching causes interference problems, the system reduces aggressiveness, creating adaptive balance.
4Use of energy by moving object
If conservative prefetching is used to reduce power consumption, then power cost is reduced, but data access speed deteriorates
Solution Approach 1:
The system dynamically adjusts prefetcher intensity based on power constraints and performance requirements. When power budget is available and performance benefits are significant, the system operates in aggressive mode. When power consumption becomes a constraint, it transitions to conservative mode to maintain acceptable power usage.
Solution Approach 2:
The system changes the operational parameters of the prefetcher to balance power consumption and data access speed. By adjusting parameters such as prefetch frequency and aggressiveness level, the system optimizes the trade-off between energy efficiency and performance.
Data Source
AI summary
In one embodiment, a system includes a processor to receive machine learning training data including label scores based on measurements of device performance during execution of benchmark applications for different prefetcher engine configurations, and corresponding device hardware states, and train configuration specific machine learning regression models based on the received machine learning training data to provide corresponding configuration specific device performance predictions based on given device hardware states, and a memory to store data used by the processor.