Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Performance analysis of embedded software using implicit path enumeration
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
A compilation-based software estimation scheme for hardware/software co-simulation
CODES '99 Proceedings of the seventh international workshop on Hardware/software codesign
Case study: system model of crane and embedded control
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Performance estimation of embedded software with instruction cache modeling
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Software performance estimation strategies in a system-level design tool
CODES '00 Proceedings of the eighth international workshop on Hardware/software codesign
Static timing analysis of embedded software on advanced processor architectures
DATE '00 Proceedings of the conference on Design, automation and test in Europe
Reliable estimation of execution time of embedded software
Proceedings of the conference on Design, automation and test in Europe
Performance analysis with confidence intervals for embedded software processes
Proceedings of the 14th international symposium on Systems synthesis
Intervals in software execution cost analysis
ISSS '00 Proceedings of the 13th international symposium on System synthesis
Making Java Work for Microcontroller Applications
IEEE Design & Test
Accurate timing analysis by modeling caches, speculation and their interaction
Proceedings of the 40th annual Design Automation Conference
A Data Analysis Method for Software Performance Prediction
Proceedings of the conference on Design, automation and test in Europe
CACO-PS: A General Purpose Cycle-Accurate Configurable Power Simulator
SBCCI '03 Proceedings of the 16th symposium on Integrated circuits and systems design
Retargetable profiling for rapid, early system-level design space exploration
Proceedings of the 41st annual Design Automation Conference
Accurate software performance estimation using domain classification and neural networks
SBCCI '04 Proceedings of the 17th symposium on Integrated circuits and system design
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Efficiently exploring architectural design spaces via predictive modeling
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
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
Journal of Systems Architecture: the EUROMICRO Journal
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High-level performance estimation of embedded software implemented in a particular processor is essential for a fast design space exploration, when the designer needs to evaluate different processor architectures (and their different versions) and also different task allocations in a multiprocessor system. The development of fast and adequate performance estimators is required to achieve the necessary speed in this design phase. However, advanced architectures present many features, such as pipelines, branch prediction mechanisms, and caches, which have a non-linear impact on the execution time, which thus becomes hard to evaluate using simple linear methods. In order to cope with this problem, this paper presents a high-level performance estimator based on a neural network, which easily adapts to the non-linear behaviour of the execution time in advanced architectures and presents a speed-up up to 190 times in comparison with cycle-accurate simulators, using the PowerPC 750 as target architecture. A method for automatic domain classification is proposed to group applications with similar characteristics, resulting in an increase of the estimation precision. For the PowerPC 750, the mean estimation error has been reduced from 7.90% to 6.41% thanks to domain-specific estimators. This precision level and the fast estimation time are suitable for high-level design space exploration, when different architectures or processor versions and different task allocations need to be evaluated in a fast way.