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ABSTRACT
We propose a technique which leverages configurable data caches to address the problem of cache interference in multitasking embedded systems. Data caches are often necessary to provide the required memory bandwidth. However, caches introduce two important problems for embedded systems. Cache outcomes in multi-tasking environments are notoriously difficult to predict, if not impossible, thus resulting in poor real-time guarantees. Additionally, caches contribute to a significant amount of power. These issues are key factors for many embedded systems. We study the effect of multiple tasks on the data cache, and propose a technique which leverages configurable cache architectures to eliminate inter-task cache interference. By mapping tasks to different cache partitions, interference is completely eliminated with only a minimal impact on performance. Furthermore, dynamic and leakage power are significantly reduced as only a subset of the cache is active at any moment. We introduce a profile-based, static analysis algorithm, which identifies a beneficial cache partitioning. The OS configures the data cache during context-switch by activating the corresponding partition.Our experiments on a large set of multitasking benchmarks demonstrate that our technique not only efficiently eliminates inter-task interference but also significantly reduces both dynamic and leakage power.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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