A Buffer-Oriented Methodology for Microarchitecture Validation
Journal of Electronic Testing: Theory and Applications - Special issue on microprocessor test and verification
On the test of microprocessor IP cores
Proceedings of the conference on Design, automation and test in Europe
Effectiveness of Microarchitecture Test Program Generation
IEEE Design & Test
Automatic Test Program Generation: A Case Study
IEEE Design & Test
Code Generation for Functional Validation of Pipelined Microprocessors
Journal of Electronic Testing: Theory and Applications
Fully Automatic Test Program Generation for Microprocessor Cores
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Automatic generation of test sets for SBST of microprocessor IP cores
SBCCI '05 Proceedings of the 18th annual symposium on Integrated circuits and system design
MicroGP—An Evolutionary Assembly Program Generator
Genetic Programming and Evolvable Machines
Software-based self-testing of microprocessors
Journal of Systems Architecture: the EUROMICRO Journal
Efficient techniques for automatic verification-oriented test set optimization
International Journal of Parallel Programming
Automatic completion and refinement of verification sets for microprocessor cores
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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We describe a rigorous ATPG-like methodology for validating the branch prediction mechanism of the PowerPC604 which can be easily generalized and made applicable to other processors. Test sequences based on finite state machine (FSM) testing are derived from small FSM-like models of the branch prediction mechanism. These sequences are translated into PowerPC instruction sequences. Simulation results show that 100\% coverage of the targeted functionality is achieved using a very small number of simulation cycles. Simulation of some real programs against the same targeted functionality produces coverages that range between 34% and 75% with four orders of magnitude more cycles. We also use mutation analysis to modify some functionality of the behavioral model to further illustrate the effectiveness of our generated sequence. Simulation results show that all 54 mutants in the branch prediction functionality can be detected by measuring transition coverage.