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1 Satisfiability modulo the Theory of Bit Vectors Alessandro Cimatti IRST, Trento, Italy Joint work with R. Bruttomesso, A. Franzen, A. Griggio, R. Sebastiani We gratefully acknowledge support from the Academic Research Program of Intel

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2 Index of the talk Satisfiability Modulo Theory The theory of Bit Vectors Satisfiability Modulo BV –Bit blasting –Eager encoding into Linear Integer Arithmetic –A lazy approach Conclusions ( A preview of QF_UFBV32 at SMT-COMP )

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3 SMT in a nutshell Satisfiability Modulo Theory –or: beyond boolean SAT Decide the satisfiability of a first order formula with respect to a background theory Examples of relevant theories –uninterpreted functions: x=y & f(x) != f(y) –difference logic: x – y < 7 –linear arithmetic: 3x + 2y < 12 –arrays: read(write(M, a0, v0) a1) –their combinations –bit vectors

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4 Why SMT From SAT-based to SMT-based verification Representation of interesting problems –timed automata –hybrid automata –pipelines –software Efficient solving –leverage availability of structural information –hopefully retaining efficiency of boolean SAT

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5 Satisfiability Modulo Theory Satisfiability: –is there a truth-assignment to boolean variables –and a valuation to individual variables –such that formula evaluates to true? Standard semantics for FOL Assignment to individual variables –Induces truth values to atoms Truth assignment to boolean atoms Induced value to whole formula

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6 Propositional structure TA P P P x y z w x

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7 Two Main Approaches to SMT the eager approach the lazy approach –theory independent view –theory specific view

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8 Eager Approach to SMT Main idea: compilation to SAT –STEP1: Theory part compiled to equisatisfiable pure SAT problem –STEP2: run propositional SAT solver

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9 Propositional structure TA P P P x y z w x

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10 P P PTA TA Lifted theoryPropositional structure

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11 The Lazy approach Ingredients –a boolean SAT solver –a theory solver The boolean solver is modified to enumerate boolean (partial) models The theory solver is used to Check for theory consistency

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12 Propositional structure TA P P P x y z w x TA TA

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13 MathSAT: intuitions Two ingredients: boolean search and theory reasoning –find boolean model theory atoms treated as boolean atoms truth values to boolean and theory atoms model propositionally satisfies the formula –check consistency wrt theory set of constraints induced by truth values to theory atoms existence of values to theory variables Example: (P v (x = 3)) & (Q v (x – y < 1)) & (y < 2) & (P xor Q) Boolean model –!P, (x = 3), Q, (x – y < 1), (y < 2) –Check (x = 3), (x – y < 1), (y < 2) –Theory contradiction! Another boolean model –P, !(x = 3), !Q, (x – y < 1), (y < 2) –Check !(x = 3), (x – y < 1), (y < 2) –Consistent: e.g. x := 0, y := 0

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14 T SAT! Boolean SAT: search space P The DPLL procedure Incremental construction of satisfying assignment Backtrack/backjump on conflict Learn reason for conflict Splitting heuristics Q R S S R T Q S T R

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15 MathSAT: approach DPLL-based enumeration of boolean models –Retain all propositional optimizations Conflict-directed backjumping, learning –No overhead if no theory reasoning Tight integration between –boolean reasoning and –theory reasoning

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16 Bool Bool T Math Bool Bool T Math Bool Bool T Math T SAT! MathSAT: search space P Many boolean models are not theory consistent! Q R S S R T Q S T R

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17 Bool Bool T Math T SAT! EP:Math EP:Math T Pruned away in the EP step Early pruning Check theory consistency of partial assignments P Q S T R

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18 THEORY OF FIXED-WIDTH BIT VECTORS

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19 Bit Vectors: Example LTmp0 = a; LTmp1 = 2 * b; LTmp2 = LTmp0 + LTmp1; LTmp3 = 4 * c; LTmp4 = LTmp2 + LTmp3; LTmp5 = 8 * d; LOut = LTmp4 + LTmp5; Are they equivalent? ((a + 2b) + 4c) + 8d RTmp0 = d; RTmp1 = RTmp0 << 1; RTmp2 = c + RTmp1; RTmp3 = RTmp2 << 1; RTmp4 = b + RTmp3; RTmp5 = RTmp4 << 1; ROut = a + RTmp5; a + ((b + ((c + (d<<1)) <<1)) <<1) input a, b, c, d : reg[N]; I.e. LOut = ROut ?

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20 Fixed Width Bit Vectors Constants –0b , 0xFFFF, … Variables –valued over BitVectors of corresponding width –implicit restriction to finite domain Function symbols –selection: x[15:0] –concatenation: y :: z –bitwise operators: x && y, z || w, … –arithmetic operators: x + y, z * w, … –shifting: x > 3 Predicate symbols –comparators: =,, >, <,,

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21 Fragments of BV theory Core –selection –concatenation Bitwise operators –x && y, x || y, x ^ y Arithmetic operators –x +y, x – y, c * x Core + Bitwise + Arithmetic Complexity of equality between BV terms –Core is in P –Core + B + A in NP Variable width bit vectors: not covered here –core is in NP –small additions yield undecidability

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22 Decision procedures for BV Many approaches –Cyrluk, Moeller, Ruess –Moeller, Ruess –Bjørner, Pichora –Barrett, Dill, Levitt Focus on deciding conjunctions of literals Emphasis on proof obligations in ITP –some emphasis on variable width, generic wrt N Shostak-style integration –canonization –solving

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23 SATISFIABILITY MODULO THEORY OF BIT VECTORS

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24 Satisfiability modulo Bit Vectors Applications of interest –RTL hardware descriptions essentially bit vectors –assembly-level programs –software with finite precision arithmetic Key feature –combination of control flow and data flow In principle, boolean logic can be encoded into BV –control (boolean logic) encoded into width 1 BVs. –Likely inefficient in comparison to SAT More natural to keep them separate at modeling –structural info can be exploited for verification

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25 Approaches to SMT(BV) Bit blasting Eager Encoding into LA Lazy approach

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26 SMT(BV) via Bit Blasting

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27 SMT(BV) via Bit Blasting Boolean variables: untouched Bit vector variables as collections of (unrelated) boolean variables –[x0, x1, …, x63] Selection/concatenations are trivial –static detection Equalities / Assignments: x = y –(x0 y0) & (x1 y1) & … & (x63 y63) Bitwise operators: x && y –[x0 & y0, x1 & y1, …, x63 & y63] Arithmetic operators: x + y –BVADD([x0, …, x63], [y0, …, y63])

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28 Comparison of Data Paths LTmp0 = a; LTmp1 = 2 * b; LTmp2 = LTmp0 + LTmp1; LTmp3 = 4 * c; LTmp4 = LTmp2 + LTmp3; LTmp5 = 8 * d; LOut = LTmp4 + LTmp5; Are they equivalent? ((a + 2b) + 4c) + 8d RTmp0 = d; RTmp1 = RTmp0 << 1; RTmp2 = c + RTmp1; RTmp3 = RTmp2 << 1; RTmp4 = b + RTmp3; RTmp5 = RTmp4 << 1; ROut = a + RTmp5; a + ((b + ((c + (d<<1)) <<1)) <<1) input a, b, c, d : reg[N]; I.e. LOut = ROut ?

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29 Bit Blasting Words a,b,c,d,… –blasted to [a1,…aN], [b1,…bN], [c1,…cN], [d1,…dN], … LTmp6 != RTmp6 –(LOut.1 != ROut.1) or … or (LOut.N != ROut.N) LTmp1 = 2 * b –formula in 2N vars, conjunction of N iffs LTmp2 = LTmp0 + LTmp1 –formula relating 3N vars –possibly additional vars required (e.g. carries) N = 16 bits? –13 secs N = 32 bits? –170 secs But obviously N = 64 bits! –stopped after 2h CPU time Scalability with respect to N???

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30 Bit-Blasting: Pros and Conses Bottlenecks –dependency on word width –wrong level of abstraction boolean synthesis of arithmetic circuits assignments are pervasive conflicts are very fine grained –e.g. discover x < y Advantages –let the SAT solver do all the work and nowadays SAT solvers are tough nuts to crack –amalgamation of the decision process no distinction between control and data conflicts can be as fine grained as possible –built-in capability to generate new atoms

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31 Enhancements to BitBlasting Tuning SAT solver on structural information –e.g. splitting heuristic for adders Preprocessing + SAT [GBD05] –rewrite and normalize bit vector terms –bit blasting to SAT

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32 SMT(BV) via reduction to SMT(LA)

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33 From BV to LIA RTL-Datapath Verification using Integer Linear Programming [BD01] BV constants as integers –0b32_1111 as 15 BV variables as integer valued variables, with range constraints –reg x [31:0] as x in range [0, 2^32) Assignments treated as equality, e.g. x = y Arithmetic, e.g. z = x + y –Linear arithmetic? not quite! BV Arithmetic is modulo 2^N –z = x + y - 2^N s, with z in [0, 2^N) Concatenation: x :: y as 2^n x + y Selection: relational encoding (based on integrity) –x[23:16] as xm, where –x = 2^24 xh + 2^16 xm + xl, xl in [0, 2^16), xm in [0, 2^8), xl in [0, 2^8) Bitwise operators –based on selection of individual bits SOLVER –the omega test

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34 From SMT(BV) into SMT(LIA) Generalizes [BD01] to deal with boolean structure Eager encoding into SMT(LIA) Unfortunately, not very efficient More precisely, a failure

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35 Retrospective Analysis Crazy approach? –Arithmetic Linear arithmetic? not quite! BV Arithmetic is modulo 2^N –Selection and Concatenation an easy problem becomes expensive! –Bitwise operators HARD!!! Available solvers not adequate –integers with infinite precision –reasoning with integers may be hard (e.g. BnB within real relaxation) Functional dependencies are lost! A clear culprit: static encoding –depending on control flow, same signal is split in different parts –z = if P then x[7:0] :: y[3:0] else x[5:2] :: y[10:3] x, y and also z are split more than needed the notion of maximal chunk depends on P !!!

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36 SMT(BV) via online BV reasoning

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37 A lazy approach Based on standard MathSAT schema –DPLL-based model enumeation –Dedicated Solver for Bit vectors The encoding leverages information resulting from decisions –Given values to control variables, the data path is easier to deal with (e.g. maximal chunks are bigger) Layering in the theory solver –equality reasoning –limited simplification rules –full blown bit vector solver only at the end

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38 BV solver The architecture Boolean enumeration EUF reasoning BV rewriter LIA encoding

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39 Rewriting rules evaluation of constant terms –0b8_ [4:2] becomes 0b3_101 rules for equality –x = y and Phi(x) becomes Phi(y) –based on congruence closure splitting concatenations –(x :: y) = z becomes x = z[h_n] && y == z[l_n]

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40 Rewriting rules pushing selections –(x && y)[7:0] becomes (x[7:0] && y[7:0]) –(x :: y)[23:8] becomes (x[7:0] :: y[15:8]) pigeon-hole rules –from (x != 0 & x != 1 & x != 2 & x < 3) derive false

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41 BV rewriter Rules are applied until –fix point reached –contradiction found Implementation based on EUF reasoner –rules as merges between eq classes Open issues –incrementality/backtrackability –selective rule activation –conflic set reconstruction When it fails …

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42 LIA encoding (the last hope) LIA encoding –idenfication of maximal slices –purification: separating out arithmetic and BW by introduction of additional variables NB: on resulting problems –LIA encoding always superior to bit blasting!!! –cfr [DB01]

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43 Status of Implementation Implementation still in prototypical state Does a lot of stupid things –conflict minimization by deletion filtering –checking that conflict are in fact minimal –unnecessary calls to LA for SAT clusters –calling LA solver implemented as dump on file, and run external MathSAT –huge conflict sets

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44 A very very preliminary evaluation

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45 Competitors Run against MiniSAT 1.14 –~ winner of SAT competition in 2005 KEY REMARK: –boolean methods are very mature A good reason for giving up?

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46 Test benches 74 benchmarks from industrial partner –would have been ideal for SMT-COMP QF_UFBV32 Unfortunately –can not be disclosed –will have to be destroyed after the collaboration –hopefully our lives will be spared

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49 Conclusions A market need for SMT(BV) solvers Bit Blasting: tough competitors After a failure, … Preliminary results are encouraging Future challenges –optimize BV solver –better conflict sets –tackle some RTL verification cases –extension to memories

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50 A small digression on QF_UFBV32 at SMT-COMP

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51 QF_UFBV[32] at SMT-COMP the MathSAT you will see there IS NOT the one I described We currently have no results for QF_UFBV Easy benchmarks: –QF_UFBV[32] not particularly SMT –the boolean component is nearly missing –the BV part is easily solvable by bit blasting We entered SMT-COMP QF_UFBV32 –MathSAT based on BIT BLASTING to SAT –NuSMV based on bit blasting to BDDs

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52 QF_UFBV: Bit Blasting to SAT Preprocessing based on –Ackermans elimination of function symbols –rewriting simplification –bit blasting Core: call SAT solver underlying MathSAT –every SAT problem in < 0.3 secs –most UNSAT within seconds –a handful of hard ones between 300 and 500 secs

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53 BDDs (???) on SMT-COMP tests Even NuSMV entered SMT-COMP Ackermans elimination of functional symbols Rewriting preprocessor Core solver –based on BDDs –conjunctively partitioned problem –structural BDD-based ordering (bit interleaving) –(almost) no dynamic reordering –affinity-based clustering, threshold 100 nodes –early quantification Seems to work well both on SAT and UNSAT instances

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54 RESULTS first STP then YICES then NuSMV then CVC3 (but no results on two samples) then MathSAT BITBLASTING –3 rd on SAT –last on UNSAT

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55 SAT instances

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56 UNSAT instances

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