🚀 go-pugleaf

I couldn't think of a more appropriate news group to post this in, so
here goze:

What sort of algorithms are typically used to minimize logic equations
specified in high level hardware description languages (PAL software,
Verilog, etc.)?

I've read and understand Quine-McClusky, and doodled around with
implementing my own versions.  It seems to work well except for the
final step: choosing a set of prime implicants from the set that you
just produced (by reductions of the form x * y + x * ~y = x, for those
of you who don't remember.  Prime implicants are those product terms
that can't be reduced any further by the application of the above
identity).

The book that I read that had this in it does the following: it
enumerates all the minterms used by the original function and finds a
set of prime implicants that covers all the minterms.

A few problems that I ran into when I fiddling around:

(1) Often the input function isn't specified as a sum of minterms.
It's specified as a sum of product terms which may or may not be
minterms.  Some of the minterms have usually been combined or reduced
already.  Generating all the minterms could be infeasible for
functions will a large number of variables.  For instance, if you
think about it, a function with 32 variables could easily generate
over 1 million minterms, as 2^32 is 4G.  Representing this with any
sort of data structure could easily exhaust all your memory, physical
or virtual.

(2) If the minterms can't be enumerated, it doesn't seem like the
prime implicants that have been generated can be tested for coverage
against the minterms.  Minterms are needed to give a shot at an
optimal solution.  Testing for coverage against the input product
terms, which remember may be prereduced terms, not minterms, could
lead to a very non-optimal selection of prime implicants.  For
instance, in the worst case, the routine might be handed the set of
prime implicants to start with.  If the resulting prime implicants of
the reduction process (which are the exact same ones are were input to
the routine since no reduction is possible because they are prime
implicants) are tested against the input set, the routine would
determine that all the prime implicants would be necessary to cover
the input pterms.  Clearly, this may not be the case, and a suboptimal
solution would result.

(3) Even if the minterms can be enumerated, it seems like finding the
set of optimal prime implicants is on the order of n!, where n is the
number of prime implicants left after the reduction.  Depending on the
number of variables and the function, this could be a lot.  How is, or
rather, is this done in a reasonable time?  Are optimal solutions
sought?  Are heuristics applied, with suboptimal solutions derived, as
a result?

So I guess my question is, how are these problems overcome?  Do people
use other algorithms than Quine-McClusky instead that don't suffer
these problems?  If so, what are they?

Does someone have references of articles that describe this stuff?
Articles themselves?

What about publicly available source code that implements the various
algorithms?

Thanks for any help people can give to satisfy my curiousity.

Dave Roberts
roberts@brahms.amd.com