Closed wangw18 closed 1 year ago
Sorry I do not understand your question.
Suppose there is a 1x16 tuple tup composed of natural numbers , and the total number of full permutation result of it is around 10^16. Take these results into self.add_rand("a",tup), as long as the constraints do not conflict, there would be bins that satisfy the constraints, but too many samples would lead to memory overflow. I would like to ask if there is a better solution to this case, could cocotb_coverage generate bins with complex constraints and large scale like sv
First of all, it is not SV that resolves complex constraints - it is a constraint solver embedded in the tool you are using. They may be better or worse solvers. Usually, good practice is not to use high complexity constraints, please look into the tutorial: https://cocotb-coverage.readthedocs.io/en/latest/tutorials.html#randomization-order-and-performance-issues
Thanks, got it
Hi sir I want to add some constraint to a 1x16 tuple using "self.add_rand("a",tup)" with tup = [(1,2,3..,16), ()..., ()], but if I want to cover all cases, the possibility of tup would be greater than "16!". Is there any other solution to solve it?