Simulation is widely used for validation of Register-Transfer-Level (RTL) models. While simulating with millions of random or constrained-random tests can cover majority of the functional scenarios, the number of remaining scenarios can still be huge (hundreds or thousands) in case of today's industrial designs. Hard-to-activate branches are one of the major contributors for such remaining/untested scenarios. While directed test generation techniques using formal methods are promising in activating branches, it is infeasible to apply them on large designs due to state space explosion. In this paper, we propose a fully automated and scalable approach to cover the hard-to-activate branches using concolic testing of RTL models. While application of concolic testing on hardware designs has shown some promising results in improving the overall coverage, they are not designed to activate specific targets such as uncovered corner cases and rare scenarios. This paper makes two important contributions. (1) We propose a directed test generation technique to activate a target by effective utilization of concolic testing on RTL models. (2) We develop efficient learning and clustering techniques to minimize the overlapping searches across targets to drastically reduce the overall test generation effort.
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Directed Test Generation for Hardware Validation: A Survey
The complexity of hardware designs has increased over the years due to the rapid advancement of technology coupled with the need to support diverse and complex features. The increasing design complexity directly translates to difficulty in verifying functional behaviors as well as non-functional requirements. Simulation is the most widely used form of validation using both random and constrained-random test patterns. The random nature of test sequences can cover a vast majority of scenarios, however, it can introduce unacceptable overhead to cover all possible functional and non-functional scenarios. Directed tests are promising to cover the remaining corner cases and hard-to-detect scenarios. Manual development of directed tests can be time-consuming and error-prone. A promising avenue is to perform automated generation of directed tests. In this article, we provide a comprehensive survey of directed test generation techniques for hardware validation. Specifically, we first introduce the complexity of hardware verification to highlight the need for directed test generation. Next, we describe directed test generation using various automated techniques, including formal methods, concolic testing, and machine learning. Finally, we discuss how to effectively utilize the generated test patterns in different validation scenarios, including pre-silicon functional validation, post-silicon debug, as well as validation of non-functional requirements.
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- Award ID(s):
- 1908131
- PAR ID:
- 10559058
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Computing Surveys
- Volume:
- 56
- Issue:
- 5
- ISSN:
- 0360-0300
- Page Range / eLocation ID:
- 1 to 36
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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