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Keywords
(13)
Analytical Model
bayesian method
Data Model
Hybrid Approach
Indexing Terms
Instruction Cache
Learning Model
Mathematical Model
Prediction Model
Statistical Approach
Worst Case Execution Time
bayesian network
Real Time Systems
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Probabilistic Instruction Cache Analysis Using Bayesian Networks
Probabilistic Instruction Cache Analysis Using Bayesian Networks,10.1109/RTCSA.2011.55,Mark Bartlett,Iain Bate,James Cussens,Dimitar Kazakov
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Probabilistic Instruction Cache Analysis Using Bayesian Networks
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Mark Bartlett
,
Iain Bate
,
James Cussens
,
Dimitar Kazakov
Current approaches to
instruction cache
analysis for determining worstcase execution time rely on building a
mathematical model
of the cache that tracks its contents at all points in the program. This requires perfect knowledge of the functional behaviour of the cache and may result in extreme complexity and pessimism if many alternative paths through code sections are possible. To overcome these issues, this paper proposes a new
hybrid approach
in which information obtained from program traces is used to automate the construction of a model of how the cache is used. The resulting model involves the learning of a
Bayesian network
that predicts which instructions result in cache misses as a function of previously taken paths. The model can then be utilised to predict cache misses for previously unseen inputs and paths. The accuracy of this learned model is assessed against real benchmarks and an established
statistical approach
to illustrate its benefits. Index Termsâ€”instruction cache; worstcase execution time (WCET);
Bayesian network
Conference:
RealTime Computing Systems and Applications  RTCSA
, vol. 1, pp. 233242, 2011
DOI:
10.1109/RTCSA.2011.55
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References
(15)
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(
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Instruction Cache Prediction Using Bayesian Networks
(
Citations: 2
)
Mark Bartlett
,
Iain Bate
,
James Cussens
Conference:
European Conference on Artificial Intelligence  ECAI
, pp. 10991100, 2010
Learning Bayesian Networks for Improved Instruction Cache Analysis
(
Citations: 1
)
Mark Bartlett
,
Iain Bate
,
James Cussens
Conference:
International Conference on Machine Learning and Applications  ICMLA
, pp. 417423, 2010