BDD Based Upon Shannon Expansion

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1 Boolean Function Manipulation OBDD and more BDD Based Upon Shannon Expansion Notations f(x, x 2,, x n )  ninput function, x i = or f xi=b (x,, x n ) = f(x,,x i,b,x i+,,x n ), b= or Shannon Expansion fix a variable f = x i f xi= (x,, x n ) + x i f xi= (x,, x n )
2 Example f = x x2 + x4 + x x2 x5 f(x=) = x4 + x2 x5 f(x=) = x2 + x4 f = x ( x4 + x2 x5) + x (x2 + x4) Similarly f = x2 ( x4) + x2 (x + x4 + x x5) To represent f, we use a divideandconquer approach to represent f(xi=) and f(xi=) xi f(xi=) f(xi=) f = x x2 + Decision Tree f = x (x2 + ) + x () g = x2 () + x2 () g2 = x2 g g2 x x2 g2 f g In f, g2 appears twice, appears 3 times, and appears twice 2
3 OBDD From root (x) to a terminal vertex ( or ), the ordering of the splitting variables is the same Each subfunction appears only once two subfunctions are the same iff their OBDD graphs are the same canonical representation Also known as readonce branch program in comp. theory x x 2X+3=5 x2 x2 3 f 2n+ vertices Classical OBDD Example Variable ordering is not important... x x2 f = x x2... xn xn even s odd # of s 3
4 Classical OBDD Example To represent a symmetric function variable ordering is not important at level i, we need at most i vertices total nodes is < O(n 2 ) no s s 2 s 3 s Ordering Example f = x x2 + x4 + x5 x6 ordering : x x2 x4 x5 x6 (linear result) If (x x2)=, f = no need to read further! Otherwise we can forget about x x2 The memoryless effect makes BDD small A node in a BDD is a memory cell to remember the intermediate results during the computation of the function ordering 2 : x x5 x2 x4 x6 (exponential result) If (x==x5=) we need to remember them all! Because nothing can be decided based upon the information we have 4
5 Ordering and OBDD Size A node in a function graph is a place to remember important information about the computation f = x x 2 + x 3 x x (n) x n proceed in this direction when reach x 2 need to remember the value of x when reach x 3 only when x x 2 = if x x 2 =, then x x 2 can be ignored and from x 3 the computation restarts fresh OBDD Size f = x x n/2+ + x 2 x n/ x n/2 x n proceed ordering as x x 2 x 3 x 4 x (n) x n when reach x n/2 all values in x x 2 x 3 x (n)/2 will need to be remembered because a different assignment may lead to different result nothing can be ignored and nothing for sure to be said about the evaluation yet need a complete binary tree for (n)/2 variables exponential result 5
6 Satisfiability Satisfyone: find a path from to the root Satisfyall : find all different paths from to the root Satisfycount : count the # of satisfying assignments Computing OBDDs x xn Circuit Initialize all Pis Compute the result of a boolean operation as merging two OBDDs and, or, xor, etc. Use procedures Apply and Reduce Follow topological order until reaching all POs 6
7 WorstCase Example Worstcase example  multiplication f = A*B for any input ordering, OBDD(f) requires an exponential number of nodes (a n a n a ) * (b n b n b ) π is an ordering Theorem: for any π, OBDD size 2 n/8 Potential solutions wordlevel BDD Binary moment diagram (BMD) Idea of Proof No matter how you order the input variables, you can always find a cut that The width is O(n) The information flow is O(n/8) Input set Input set 2 O(n/8) 7
8 Equivalent to Say Need to remember O(n/8) different states More Terminology Composition f(xi=g)(x xn) = f(x,, g(x xn),, xn) ex. f=xx2+x4 and g=x2+ f(x=g) = x2 + x2 + x4 = x2 + x4 Dependency Set I f = { i f(xi=) f(xi=) } f depends on xi f = x x2 + x x2 f is independent of x Satisfying Set S f = { (x xn) f(x xn) = } all assignments that made f = 8
9 OBDD Operation Complexity Reduce (make canonical, minimal form)  O(G log G) (DA) graphs isomorphism Apply (merge two OBDDs)  O(G G 2 ) Boolean operation = {Apply Reduce} Restrict {f(xi=b)}  O(G log G) Compose {f(xi=g)}  O(G 2 G 2 ) SatisfyOne  O(n) Satisfyall  O(n S f ) SatisfyCount  O(G) This is a #Pcomplete problem Apply and Reduce OBDD op OBDD2 The basic algorithms to do so are Apply(OBDD, OBDD2) > BDD3 Reduce(BDD3) > OBDD3 9
10 f op f 2 = Apply x i [ f (x i =) op f 2 (x i =) ] + x i [ f (x i =) op f 2 (x i =) ] op = T T2 T3 T4 T op T3 T2 op T4 Example 2 Apply = 3 3 F= X + X X3 2 Reduce
11 Complexity of Apply i nodes op j nodes = k nodes k i * j i nodes represent i different subfunctions the worst case  (each of i nodes) op (each of j nodes) are different Total complexity O(G G2) Hash table in Apply Apply ( ), T T2 a T3 T4 b Apply(T, T3) Apply(T2, T4) Apply(a,b) When doing Apply(T,T3), we have done Apply(a,b) Apply(a,b) should not be repeated Need a hash table to store results of from Apply of all subfunctions.
12 Reduce DA Graph Isomorphism we can do a pairwise comparison for all nodes but that is too slow instead: by labeling bottomup = = 6 (4,5) 4 (3,2) 5 (,2) 3 (,2) () (2) Each graph has a unique labeling scheme same label at the root = same graph Labeling Scheme i i... i i+ i+ i+... i+2 i+2... i+2 i = i a b c d iff a=c && b=d Why working bottomup? So that when working at level i, each node below already points to a unique graph already 2
13 Redundant Nodes Removal i+ i i i = i+ i+ i i i i i+ i+ i+ = At each level, how do we know how many nodes are identical in the labeling scheme? by sorting the labels at that level O(n log n) time complexity An Example (4,3) (,3) 2 2 (3,3) 2 3 (,2) 3 (,2) 3 () (2) () (2) 3
14 Other BDDs Function Decomposition Rules OBDD f = x f(x=) + x f(x=) ReedMuller Decomposition f = f(x=) x [ f(x=) f(x=) ] f = f(x=) x [ f(x=) f(x=) ] Ordered Functional Decision Diagram x f(x=) f(x=) f(x=) 4
15 F = (x x2) F(=) = F(=) = x x2 difference D3 = x x2 D3(x2=) = x D3(x2=) = x difference D2 = OFDD x2 x OFDD requires a different Reduction rule For some functions, OFDD can be exponentially smaller than OBDD and vice verse Evaluation of the graph is different from OBDD Can no longer be simply tracing from root to s or s Free BDD The ordering along each path can be different Main issues Deciding that two trees are representing the same functions can not be done by graph isomorphism. It can only be done via a probabilistic algorithms Free BDD give flexibility in the representation but sacrifice in the complexity of manipulate the BDD date structures 5
16 Represent NumericValued Functions eg. f=x +2x +4x 2 Arithmetic Decision Diagram (ADD) Associate more values as terminal nodes EdgeValued BDD (EVBDD) Bring values from the terminal nodes to edges Binary Moment Diagram (BMD) Decomposition : f = f(x=) + x (δf/δx) Linearly Inductive BDD Linearly Inductive Function Recursively Defined X i : the inputs to the iinstance function F i : the iinstance function B i : an arbitrary boolean function F = B (x ) and F i = B i (X i, G i ) B i = B j for all i,j Example  Adder Sum = a b c_in Sum i = ai bi Carry i Carry = a b + (a+b) c_in Carry i = ai bi + (ai+bi) Carry i 6
17 LIBDD Require different rules for Apply and Reduce Applicable to special case when good sequentiality can be captured Function definition has to be known in advance Inspiration ZeroSuppressed BDD To represent a collection of bit vectors eg. () () () Such a representation is often encountered in some combinatorial problems Usually, the s in the bit vectors is sparse ZBDD Change the Reduction rule A node can be removed iff the edge points to x 7
18 x2 ZBDD Interpretation x x x x In ZBDD sense A = {, } and B = {,, } ZBDD is good for sets manipulation ZBDD can be used to represent a function as a collection of cube sets x,x2,={,,} => {,,} To ensure sparse => :, :, : 8
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