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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

Probabilistic Instruction Cache Analysis Using Bayesian Networks  
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Current approaches to instruction cache analysis for determining worst-case 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 Terms—instruction cache; worst-case execution time (WCET); Bayesian network
Conference: Real-Time Computing Systems and Applications - RTCSA , vol. 1, pp. 233-242, 2011
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