High-performance timing simulation of embedded software
Proceedings of the 45th annual Design Automation Conference
A mapping framework for guided design space exploration of heterogeneous MP-SoCs
Proceedings of the conference on Design, automation and test in Europe
MEMOCODE'09 Proceedings of the 7th IEEE/ACM international conference on Formal Methods and Models for Codesign
Performance evaluation of concurrently executing parallel applications on multi-processor systems
SAMOS'09 Proceedings of the 9th international conference on Systems, architectures, modeling and simulation
Performance modeling of embedded applications with zero architectural knowledge
CODES/ISSS '10 Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
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Estimation tools are a key component of system-level methodologies, enabling a fast design space exploration. Estimation of software performance is essential in current software-dominated embedded systems. This work proposes an integrated methodology for system design and performance analysis. An analytic approach based on neural networks is used for high-level software performance estimation. At the functional level, this analytic tool enables a fast evaluation of the performance to be obtained with selected processors, which is an essential task for the definition of a "golden" architecture. From this architectural definition, a tool that refines hardware and software interfaces produces a bus-functional model. A virtual prototype is then generated from the bus-functional model, providing a global, cycle-accurate simulation model and offering several features for design validation and detailed performance analysis. Our work thus combines an analytic approach at functional level and a simulation-based approach at bus functional level. This provides an adequate trade-off between estimation time and precision. A multiprocessor platform implementing an MPEG4 encoder is used as case study, and the analytic estimation results in errors only up to 17% when compared to the virtual platform simulation. On the other hand, the analytic estimation takes only 17 seconds, against 10 minutes using the cycle-accurate simulation model.