Solving Functional Verification Challenges using AI-driven Verification

Automation has allowed design teams to create more complex chips. This has enabled verification-managed systems? The veri?cation ?ow involves several simulations tested in regressions to exercise all the critical scenarios in the design.

Functional coverage model de?nedby the engineer at the start of the veri?cation cycle from the veri?cation plan to cover all features and corner cases in the design. Traditional methods of creating test benches using SystemVerilog and UVM are time-consuming and have a steep learning curve for new engineers. Using a higher level, the general-purpose programming language is more practical for veri?cation.?

Cocotb is an open-source testbench environment for verifying Verilog and VHDL design. It is built to lower the overhead of creating a test. It Uses Python to program test benches. It requires a simulator to simulate only the HDL design for which an open-source compiler, Icarus Verilog can be used. This eliminates the need for licenses

Machine learning inputs are seen to perform better than random inputs for veri?cation of complex ICs. This methodology is proven to greatly reduce the time for ?nding hard to hit scenarios and generating obscure test cases for coverage closure. In a veri?cation cycle, regressions are simulated multiple times for every iteration of the RTL design phase. Regressions are used to verify Gate Level Simulations(GLS). as well, which are known to be far more time-consuming than RTL simulations. Faster coverage closure will thereby exponentially reduce the total time for veri?cation over a veri?cation cycle from RTL to GLS, thus leading to quicker tape out of the chip

Coverage-Based Approach & Test Failure Based Inputs are the key approaches for machine learning/ AI Driven verification