High-level synthesis (HLS) is a powerful tool for designing digital circuits. It allows designers to quickly and easily create complex circuits from high-level descriptions. However, the quality of the resulting circuits can be difficult to verify. ETH Zurich has developed a formal verification approach to improve the quality of HLS-generated circuits.
The formal verification approach at ETH Zurich is based on a combination of formal methods and automated testing. Formal methods are used to check the correctness of the circuit design, while automated testing is used to check the performance of the circuit. This approach ensures that the circuit design is correct and that it meets the required performance specifications.
The formal verification approach at ETH Zurich also includes a number of techniques to reduce the complexity of the verification process. These techniques include model checking, theorem proving, and static analysis. Model checking is used to check the correctness of the circuit design by verifying that all possible states of the circuit are valid. Theorem proving is used to prove that certain properties of the circuit are true. Finally, static analysis is used to identify potential errors in the circuit design before it is implemented.
The formal verification approach at ETH Zurich has been successfully used to improve the quality of HLS-generated circuits. The approach has been applied to a variety of applications, including embedded systems, automotive systems, and medical devices. By using this approach, designers can ensure that their circuits are correct and meet the required performance specifications.
Overall, ETH Zurich’s formal verification approach is an effective way to improve the quality of HLS-generated circuits. The approach combines formal methods and automated testing to ensure that the circuit design is correct and meets the required performance specifications. By using this approach, designers can create high-quality circuits with confidence.
Source: Plato Data Intelligence: PlatoAiStream