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A design team at a customer of mine started
out with Specman for the first time, having dabbled with a bit of
SystemVerilog. I can't reveal any details of their design, but suffice to say
they had a fun and not-so-simple challenge for me, the outcome of which I can
share. Unlike some customers (and EDA vendors) who think it's a good test for a
solver to do sudoku or the N-Queens puzzle (see this TeamSpecman blog post /blogs/fv/archive/2011/08/18/if-only-gauss-had-intelligen-in-1850.aspx),
this team wanted to know whether IntelliGen could solve a tough real-world
The data handled by their DUT comes in as a
2D array of data bytes, which has been processed by a front-end block. The
data in the array can contain multiple errors, some of which will have been
marked as "known errors" by the front-end. Other "unknown" errors may also be
present, but provided that the total number of errors is less than the number
of FEC bytes, all the errors can and must be repaired by the DUT. If too many
errors are present, it is not even possible to detect the errors, so the
testbench must generate the errors carefully to avoid meaningless stimulus. It
also needs to differentiate between marked and unmarked errors so that the
DUT's corrections can be tested and coverage performed based on the number of
each type of error.
This puzzle is rather more complex than the
N-Queens one: we have multiple errors permitted on any single column or row in
the array, and there are three possible states for each error: none, marked and
unmarked. There is an arithmetic relationship between the error kinds: twice
the number of marked errors than unmarked can be corrected. Furthermore, unlike
the N-Queens, a test writer may wish to add further constraints such as
clustering all the errors into one row, fixing the exact number of errors, or
having only one kind of error.
First we define an enumerated type to model
the error kind:
By modelling the 2D array twice, once as
complete rows and once as complete columns, we can apply constraints to a row or
column individually, as well as to the entire array. We only look at whether to
inject an error, not what the erroneous data should be (this would be the second stage). I've only shown the row-based model here, but the column-based one is identical
bar the naming.
The row_s represents one row from the 2D
array, with each element of "col" representing one column along that row. The
constraints on num_known and num_unmarked limit how many errors will be
present. These are later connected to the column-based model in the parent
The effective_errors field and its
constraints model the relationship between the known and unmarked errors,
whereby twice as many known errors than unmarked errors can be corrected.
Next we define the parent struct which
links the row and column models to form a complete description of the problem.
Here "cols" and "rows" are the two sub-models, and the other fields provide the
top-down constraint linkage.
The intent is that the basic dimensions are set within the base environment, and the remaining controls are
used for test writing.
Next, we look at the constraints which
connect the row and column models together. The first things to do are to set
the dimensions of the arrays based on the packet dimensions, and to cross-link
the row and column models. These are structural aspects that cannot be
changed. The rest of the constraints tie together the number of errors in each
row, column, and the entire array. By using bi-directional constraints, we are
allowing the test writer to put a constraint on any aspect.
And that's it! With just that small amount
of information IntelliGen can generate meaningful distributions of errors in a
controlled way. Test writers can further refine the generated error maps with
simple constraints that are actually quite readable:
Notice another little trick here: the use
of a named constraint: "packet_mostly_correctable". This allows a test writer
to later extend the error_map_s and disable or replace this constraint by name;
far easier than figuring out the "reset_soft()" semantics and a whole lot more
Note that for best results, this problem
should be run using Specman 13.10 or later due to various improvements in the
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