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EnglishA relevant aspect in design analysis and verification is mon- itoring how logic relations among di↵erent variables change at run time. Current static approaches su↵er from scalabil- ity problems that prevent their adoption on large designs. On the contrary, dynamic techniques scale better from the memory-consumption point of view. However, to achieve a high accuracy, they require to analyse a huge number of (long) execution traces, which results in time-consuming phases. In this paper, we present a new ecient approach to automatically infer logic relations among the variables of a design implementation. Both a sequential and a GPU- oriented parallel implementation are proposed to dynami- cally extract likely invariants from execution traces on dif- ferent time windows. Execution traces composed of millions of simulation instants can be eciently analysed.
| Titolo: | A parallelizable approach for mining likely invariants |
| Autori: | |
| Data di pubblicazione: | 2015 |
| Abstract: | A relevant aspect in design analysis and verification is mon- itoring how logic relations among di↵erent variables change at run time. Current static approaches su↵er from scalabil- ity problems that prevent their adoption on large designs. On the contrary, dynamic techniques scale better from the memory-consumption point of view. However, to achieve a high accuracy, they require to analyse a huge number of (long) execution traces, which results in time-consuming phases. In this paper, we present a new ecient approach to automatically infer logic relations among the variables of a design implementation. Both a sequential and a GPU- oriented parallel implementation are proposed to dynami- cally extract likely invariants from execution traces on dif- ferent time windows. Execution traces composed of millions of simulation instants can be eciently analysed. |
| Handle: | http://hdl.handle.net/11562/928335 |
| ISBN: | 9781467383219 |
| Appare nelle tipologie: | 04.01 Contributo in atti di convegno |
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