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Eliminating inter-process cache interference through cache reconfigurability for real-time and low-power embedded multi-tasking systems
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International Conference on Compilers, Architecture and Synthesis for Embedded Systems archive
Proceedings of the 2007 international conference on Compilers, architecture, and synthesis for embedded systems table of contents
Salzburg, Austria
SESSION: Memory systems table of contents
Pages: 198 - 207  
Year of Publication: 2007
ISBN:978-1-59593-826-8
Authors
Rakesh Reddy  University of Maryland
Peter Petrov  University of Maryland
Sponsors
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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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

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Collaborative Colleagues:
Rakesh Reddy: colleagues
Peter Petrov: colleagues