Towards Performance Prediction of Compositional Models in GALS Designs. (Vers la prédiction de performance de modèles compositionnels dans les architectures GALS)

Validation, comprising functional verification and performance evaluation, is critical for complex hardware designs. Indeed, due to the high level of parallelism in modern designs, a functionally verified design may not meet its performance specifications. In addition, the later a design error is identified, the greater its cost. Thus, validation of designs should start as early as possible. This thesis proposes a compositional modeling framework, taking into account functional and time aspects of hardware systems, and defines a performance evaluation approach to analyze constructed models. The modeling framework, called Interactive Probabilistic Chain (IPC), is a discrete-time process algebra, representing delays as probabilistic phase type distributions. We defined a branching bisimulation and proved that it is a congruence with respect to parallel composition, a crucial property for compositional modeling. IPCs can be considered as a transposition of Interactive Markov Chains in a discrete-time setting, allowing a precise and compact modeling of fixed hardware delays. For performance evaluation, a fully specified IPC is transformed, assuming urgency of actions, into a discrete-time Markov chain that can then be analyzed. Additionally, we defined a performance measure, called latency, and provided an algorithm to compute its long-run average distribution. The modeling approach and the computation of latency distributions have been implemented in a toolchain relying on the Cadp toolbox. Using this toolchain, we studied communication aspects of an industrial hardware design, the xSTream architecture, developed at STMicroelectronics.