Indirect Prefetcher Training Reset for Secure Array-Indirect Access
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Solution Overview
Problem
Existing prefetchers struggle to accurately and securely prefetch irregular, array-indirect accesses in cloud workloads, leading to inefficiencies and potential security vulnerabilities.
Innovation Solution
A hardware prefetcher architecture that identifies array-indirect relationships with high success rate and minimizes power consumption by training at the commit stage, incorporating a producer-consumer pair analysis and resetting training upon execution or context changes to ensure security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware prefetching is implemented to speed up fetch operations, then system performance is improved, but security vulnerabilities and power consumption increase
Solution Approach 1:
The prefetcher performs preliminary fetch operations before data is actually needed by executing instructions ahead of time. The training logic identifies access patterns in advance, and the prefetch buffer stores predicted data beforehand, reducing latency when the data is actually required while maintaining security through controlled prediction mechanisms.
Solution Approach 2:
The system implements feedback through the training logic that continuously monitors actual memory access patterns and compares them with predicted patterns. The prefetcher uses this feedback to refine its predictions, adjusting its behavior based on whether previous prefetch operations were accurate, thereby improving security and reducing wasted power consumption.
2Measurement precision
If prefetch training is performed continuously to improve accuracy, then prefetch accuracy is improved, but power consumption increases
Solution Approach 1:
The training logic performs prefetch training periodically rather than continuously, updating access patterns at specific intervals or when significant changes are detected. This periodic training maintains adequate prefetch accuracy while significantly reducing power consumption compared to continuous training operations.
Solution Approach 2:
The prefetcher system uses self-service by leveraging existing memory access instructions that are already being executed by the processor. The training logic extracts access patterns from normal program execution without requiring separate dedicated training operations, thereby maintaining accuracy while minimizing additional power consumption.
3Measurement precision
If the prefetcher captures irregular array-indirect access patterns, then prefetch accuracy for cloud workloads is improved, but device complexity increases
Solution Approach 1:
The prefetcher employs dynamic characteristics by adapting its training and prediction behavior based on the specific access patterns detected in different cloud workloads. The training logic dynamically adjusts to irregular array-indirect access patterns rather than using a fixed approach, improving accuracy for diverse workload types while managing complexity through adaptive rather than exhaustive design.
Solution Approach 2:
The prefetcher achieves universality by designing a training logic that can handle multiple types of access patterns including regular sequential accesses, strided accesses, and irregular array-indirect accesses. This multi-functional approach captures diverse cloud workload patterns using a unified architecture rather than requiring separate specialized prefetchers for each access type.
Data Source
AI summary
Disclosed is a prefetcher, e.g., of a system with one or more cores. The prefetcher determines data dependency access (DDA) patterns, such as array indirect access, and prefetches data based on the DDA patterns. The training for the DDA patterns may take place upon an occurrence of a prefetch training reset event. The prefetch training reset event may be an execution level change or a context switch.