1. Canonical Computation without Canonical Data Structure
Alan Mishchenko Robert Brayton
Department of EECS, UC Berkeley
Ana Petkovska Mathias Soeken
Integrated Systems Laboratory, EPFL, Lausanne, Switzerland
Luca Amarú Antun Domic
Synopsys Inc., Design Group, Sunnyvale, California, USA

2. Overview Motivation Definition of canonicity
Fundamentals of SAT solving
Canonicity in SAT-based computations
for satisfiable calls (LEXSAT)
for unsatisfiable calls (LEXUNSAT)
Experiments
Conclusion 2

3. Motivation Complementary data structures: BDDs vs SAT BDDs SAT
trading space for time
BDDs
canonical, easy to use but difficult to construct (can mem out)
SAT
non-canonical, easy to construct but difficult to use (can time out)
However, SAT is “better” in most cases
Can we improve SAT by borrowing from BDDs?
How about canonicity? - Yes, we can!

4. What Is Canonicity? Canonical representation Canonical computation
Representing Boolean functions in a unique way
for a given Boolean function with a given variable order, only one representation is possible
Canonical computation
Computing Boolean functions in a unique way
for a given Boolean function with a given variable order, only one result of computation is possible
In the BDD world, we did not distinguish the two
In the SAT world, we cannot have both
However, we can have canonical computation!

5. SAT in Practical Applications
Netlist
Answer: “SAT” or “UNSAT”
CNF
SAT solver
CNF generator
CNF
Design constraints
If SAT, a counter-example
If UNSAT, a core
User cost functions
Both counter-examples and cores are useful in SAT-based applications.
In practice, cores are often represented as subsets of assumptions that make the problem UNSAT.

6. Incremental SAT
Initial CNF
Round 1:
SAT solver
Initial assumptions
Additional CNF
Round 2:
SAT solver
New assumptions
Additional CNF
Round 3:
SAT solver
New assumptions
Assumptions are CNF clauses used only in the current round – they are handled differently from the rest of CNF clauses.

7. Proposed Modification to SAT
Traditional:
Input: CNF, assumptions
Output:
(1) satisfying assignment or
(2) UNSAT core, that is, a subset of literals that make the instance unsatisfiable
Proposed:
Input: CNF, assumptions, variable order
(1) canonical satisfying assignment or
(2) canonical UNSAT core, that is, subset of literals that make the instance unsatisfiable

8. The Main Idea Canonicity is achieved by adopting a variable order
Now all satisfying assignments can be compared, and the smallest one can be returned
Similarly, all UNSAT cores can be compared, and the smallest one can be returned

9. Choosing The Smallest
Since we have a variable order, we order all assignments (cores) using this order, and take the first one in the list
All satisfying assignments:
 the smallest one …
Fortunately, there is no need to compute all assignments in order to find the minimum one

10. LEXSAT
Input: cnf F
Output: the smallest satisfying assignment as literals in array A
array LEXSAT( cnf F ) {
Initialize array A to have all negative literals in the given order;
for ( i = 0; i <|A|; i++ ) {
if ( F is UNSAT under assumptions A[0] through A[i] )
invert the polarity of literal A[i] to be positive; }
return A;

11. LEXUNSAT
Input: cnf F
Output: the smallest satisfying assignment as literals in array A
array LEXUNSAT( cnf F ) {
Initialize array A to have all positive literals in the given order;
for ( i = 0; i <|A|; i++ ) {
if ( F is UNSAT under assumptions in A without A[i] )
invert the polarity of literal A[i] to be negative; }
return A;

12. Applications of LEX{SAT,UNSAT}
Canonical SAT-based ISOP (similar to BDD-based ISOP)
useful in collapsing/refactoring, timing-driven optimization, etc
Computing minimal supports in node minimization, resubstitution, Boolean decomposition, ECO
Diversifying SAT assignments
useful in both logic synthesis and formal verification
Approximate computing, SMT solving, etc

13. ISOP Computations Compared
BDDovo
BDD-based ISOP for the original variable order without dynamic variable reordering
BDDdvr
BDD-based ISOP for the original variable order with dynamic variable reordering
SATovo
SAT-based ISOP with LEXSAT and LEXUNSAT for the original variable order
SATrvo
SAT-based ISOP with LEXSAT and LEXUNSAT for a random variable order

14. Computing Canonical ISOP

15. Counter-Example Minimization

16. Conclusion Reviewed BDDs and SAT
distinguished canonical representation and canonical computation
Showed that SAT is capable of canonical computations
discussed two algorithms (LEXSAT and LEXUNSAT)
Listed several practical applications
most of them in logic synthesis

17. Abstract
A computation is canonical if the result depends only on the Boolean function and a selected variable order, and does not depend on how the function is represented and how the computation is implemented.
In the context of Boolean satisfiability (SAT), canonicity implies that the result (a satisfying assignment for satisfiable instances and a UNSAT core for unsatisfiable ones) does not depend on the circuit structure, CNF generation algorithm, and the SAT solver used.
The main highlight of this paper is that all SAT-based computations can be made canonical without building a canonical data-structure. The runtime overhead for inducing canonicity is relatively small and is often justifies by the uniqueness and the improved quality of results.