%0 Conference Paper %1 schwachhofer2024optimizing %A Schwachhofer, Denis %A Angione, Francesco %A Becker, Steffen %A Wagner, Stefan %A Sauer, Matthias %A Bernardi, Paolo %A Polian, Ilia %B 2024 IEEE European Test Symposium (ETS) %D 2024 %K RISC-V System-LevelTest genetiProgramming geneticAlgorithms stressTest testGeneration %P 1-4 %R 10.1109/ETS61313.2024.10567817 %T Optimizing System-Level Test Program Generation via Genetic Programming %U http://dx.doi.org/10.1109/ets61313.2024.10567817 %X The rising complexity of integrated devices has led to new defect types and failure modes at the system level that are not detected by structural tests. System-Level Test (SLT) is another test step to combat this challenge. SLT is in charge of exercising system-level interactions between hardware components and software. Non-functional properties, e.g., temperature, play a major role in SLT.This work focuses on the automatic generation of assembly test programs for SLT that aim to indirectly maximize a particular non-functional property, for example, the temperature. It is based on two-step generation with genetic algorithms. First, a fast architectural simulation is used with the genetic algorithm to provide a structure for the test programs. Afterward, an additional generation is done on the hardware to optimize the initial register contents of the program.The case study for gathering experimental results is a super-scalar out-of-order RISC-V processor, the Berkeley Out-of-Order Machine (BOOM). Experimental results show that the two-step generation is more effective in converging to a better power-hungry test program than only using the power consumption as a fitness function for the genetic algorithm.

@inproceedings{schwachhofer2024optimizing, abstract = {The rising complexity of integrated devices has led to new defect types and failure modes at the system level that are not detected by structural tests. System-Level Test (SLT) is another test step to combat this challenge. SLT is in charge of exercising system-level interactions between hardware components and software. Non-functional properties, e.g., temperature, play a major role in SLT.This work focuses on the automatic generation of assembly test programs for SLT that aim to indirectly maximize a particular non-functional property, for example, the temperature. It is based on two-step generation with genetic algorithms. First, a fast architectural simulation is used with the genetic algorithm to provide a structure for the test programs. Afterward, an additional generation is done on the hardware to optimize the initial register contents of the program.The case study for gathering experimental results is a super-scalar out-of-order RISC-V processor, the Berkeley Out-of-Order Machine (BOOM). Experimental results show that the two-step generation is more effective in converging to a better power-hungry test program than only using the power consumption as a fitness function for the genetic algorithm.}, added-at = {2024-09-12T16:38:36.000+0200}, author = {Schwachhofer, Denis and Angione, Francesco and Becker, Steffen and Wagner, Stefan and Sauer, Matthias and Bernardi, Paolo and Polian, Ilia}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/261465ac6403a9ff199b6ee85f7d3dfcf/dschwachhofer}, booktitle = {2024 IEEE European Test Symposium (ETS)}, doi = {10.1109/ETS61313.2024.10567817}, interhash = {83f82058736c95d624b41163fc208d75}, intrahash = {61465ac6403a9ff199b6ee85f7d3dfcf}, issn = {1558-1780}, keywords = {RISC-V System-LevelTest genetiProgramming geneticAlgorithms stressTest testGeneration}, month = may, pages = {1-4}, timestamp = {2024-09-12T16:38:36.000+0200}, title = {Optimizing System-Level Test Program Generation via Genetic Programming}, url = {http://dx.doi.org/10.1109/ets61313.2024.10567817}, year = 2024 }