Don't over-constrain in formal property verification (FPV) flows
Anders Nordstrom, Synopsys
EDN (February 04, 2016)
Formal property verification (FPV) is increasingly being used to complement simulation for system-on-chip (SoC) verification. Adding FPV to your verification flow can greatly accelerate verification closure and find tough corner-case bugs, but it is important to understand the differences between the technologies. The main difference is that FPV uses properties, i.e., assertions and constraints, instead of a testbench. Assertions are used in simulation as well, but the role of constraints is different. An understanding of constraints is necessary for successful use of FPV.
Constraints
Constraints play a central role in FPV. They define what is legal stimulus to the design under test, i.e., what state space can be reached. Assertions define the desired behavior of the DUT for the legal stimulus.
Constraints describe how inputs to the DUT are allowed to behave, what values should be taken, and temporal relationships between inputs. Constraints can be thought of as the stimulus in simulation. In constrained random simulation, the constraint solver generates an input vector for the next cycle that satisfies all constraints. It will continue to generate cycle after cycle of stimulus until the end of simulation, or until it reaches a situation where no legal stimulus can be generated.
In contrast, constraints for formal verification can describe, for example, how to legally communicate within a given protocol.
To read the full article, click here
Related Semiconductor IP
Related Articles
- Formal property verification: A tale of two methods
- How formal verification saves time in digital IP design
- Formal Verification Has It Covered!
- SoC Functional verification flow
Latest Articles
- Automated Multi-Line Bug Repair Using Model Checker Aided LLMs: A Case Study on RISC-V Processors
- Cryogenic Recharacterization of a Commercial 22 nm FD-SOI Foundry Standard-Cell Library
- A 105 MS/s 4.18 nJ/inference 0.765 mm² Mixed-Signal Streaming ML Engine for Wireless Baseband in 12LP+ FinFET
- Agentic AI for Heterogeneous System Design Across Chip, Scale-Up, and Scale-Out Domains
- Minimum RISC-V: Maximum FPGA Efficiency