ECE 667 Synthesis & Verification - BDD 1 ECE 667 ECE 667 Synthesis and Verification of Digital Systems Binary Decision Diagrams (BDD)

ECE 667 Synthesis & Verification - BDD 2 Outline Background – –Canonical representations BDD’s – –Reduction rules – –Construction of BDD’s – –Logic manipulation of BDD’s – –Application to verification and SAT Reading: read one of the BDD tutorials available on class web site – –Anderson, or – –Somenzi

ECE 667 Synthesis & Verification - BDD 3 Common Representations Boolean functions ( f : B  B ) – –Truth table, Karnaugh map – –SoP, PoS, ESoP – –Reed-Muller expansions (XOR-based) – –Decision diagrams (BDD, ZDD, etc.) Each minimal, canonical representation is characterized by – –Decomposition type Shannon, Davio, moment decomposition, Taylor exp., etc. – –Reduction rules Redundant nodes, isomorphic sub-graphs, etc. – –Composition method (“Apply”, compose rule) What they represent – –Boolean functions (f : B  B) – –Arithmetic functions (f : B  Int ) – –Algebraic expressions (f : Int  Int )

ECE 667 Synthesis & Verification - BDD 4 Binary Decision Diagrams ( BDD ) Based on recursive Shannon expansion f = x f x + x’ f x’ Compact data structure for Boolean logic – –can represents sets of objects (states) encoded as Boolean functions Canonical representation – –reduced ordered BDDs (ROBDD) are canonical – –essential for verification

ECE 667 Synthesis & Verification - BDD 5 ROBDD’s Directed acyclic graph (DAG) One root node, two terminal nodes 0, 1 (sinks) Each node has exactly two children, associated with a variable Shannon co-factoring tree, except reduced and ordered (ROBDD) – –Reduced: any node with two identical children is removed two nodes with isomorphic BDD’s are merged – –Ordered: Co-factoring variables (splitting variables) always follow the same order along all paths x i 1 < x i 2 < x i 3 < … < x i n

ECE 667 Synthesis & Verification - BDD 6 BDD Example Two different orderings, same function. f = ab+a’c+bc’d 1 0 a bb cc d 0 1 c+bd b Root node c+d c d a c d b 01 c+bd d*b b

ECE 667 Synthesis & Verification - BDD 7 ROBDD Ordered BDD (OBDD): Input variables are ordered - each path from root to sink visits nodes with labels (variables) in the same order. ordered {a,c,b} Reduced Ordered BDD (ROBDD) - reduction rules: –if the two children of a node are the same, the node is eliminated: f = v f + v’ f –if two nodes have isomorphic graphs, they are replaced by one of them These two rules make it so that each node represents a distinct logic function. not ordered a b c c 0 1 b a cc b 0 1 Not reduced !

ECE 667 Synthesis & Verification - BDD 8 Efficient Implementation of BDD’s BDDs is a compressed Shannon co-factoring tree: f = v f v + v f v leafs are constants “0” and “1” Three components make ROBDDs canonical (Proof: Bryant 1986): – –unique nodes for constant “0” and “1” – –identical order along each path – –hash table that ensures: (node(f v ) = node(g v ))  (node(f v ) = node(g v ))  node(f) = node(g) – –provides recursive argument that node(f) is unique when using the unique hash-table v 0 1 f fvfv fvfv

ECE 667 Synthesis & Verification - BDD 9 Onset is Given by all Paths to “1” Notes: By tracing paths to the 1 node, we get a cover of pairwise disjoint cubes. The power of the BDD representation is that it does not explicitly enumerate all paths; rather it represents paths by a graph whose size is measured by the number of the nodes, and not paths. A DAG can represent an exponential number of paths with a linear size (number of nodes) in terms of its variables. BDDs can be used to efficiently represent sets – –interpret elements of the onset as elements of the set – –f is called the characteristic function of that set F = b’+a’c’ = ab’+a’cb’+a’c’ BDD encodes all paths to the 1 node a c b f f a = b’ f a = cb’+c’

ECE 667 Synthesis & Verification - BDD 10 Implementation Variables are totally ordered : If v < w then v occurs “higher” up in the ROBDD Top variable of a function f is a variable associated with its root node. a b 01 f b is top variable of f b 01 f reduced Reduction (redundant node) f a = b, f  a = b f does not depend on a since f a = f  a. v f 01 h g 10 f v g h MUX v is top variable of f Each node is written as a triple: f = (v,g,h), where g = f v and h = f  v. We read this triple as: f = if v then g else h = ite (v,g,h) = vg+v ’ h

ECE 667 Synthesis & Verification - BDD 11 BDD Construction – naïve way Reduced Ordered BDDReduced Ordered BDD 1 edge 0 edge a b c f Truth table f = ac + bc Decision tree a b c b ccc f

ECE 667 Synthesis & Verification - BDD 12 BDD Reduction Rules -1 Eliminate redundant nodesEliminate redundant nodes (with both edges pointing to same node) f = a g(b) + a’ g(b) = g(b) b g a b f g

ECE 667 Synthesis & Verification - BDD 13 BDD Reduction Rules -2 Merge duplicate nodes (isomorphic subgraphs)Merge duplicate nodes (isomorphic subgraphs) Nodes must be unique f 1 = f a’ g(b) + f a h(c) = f 2 f = f 1 = f 2 aa bc h g f1f1 f2f2 a bc g h f

ECE 667 Synthesis & Verification - BDD 14 BDD Construction – cont’d 10 a b c b ccc ff 10 a b c b c 10 a b c f = (a+b)c 2. Merge duplicate nodes 1. Merge terminal nodes 3. Remove redundant nodes

ECE 667 Synthesis & Verification - BDD 15 BDD Construction – the right way f = (a + b) c

ECE 667 Synthesis & Verification - BDD 16 Logic Manipulation using BDDs Useful operators –Complement ¬ F = F’ (switch the terminal nodes) –Restrict: F| x=b = F(x=b) where b = const 0 1 F(x,y) x=b 0 1 F(y) Restrict To restrict variable x to 1, reconnect all incoming edges to nodes x to their 1-nodes To restrict variable x to 0, reconnect all incoming edges to nodes x to their 0-nodes ¬ FF’ –Complement ¬ F = F’ (switch the terminal nodes) –Restrict: F| x=b = F(x=b) where b = const 0 1 F(x,y) x=b 0 1 F(y) Restrict To restrict variable x to 1, reconnect all incoming edges to nodes x to their 1-nodes To restrict variable x to 0, reconnect all incoming edges to nodes x to their 0-nodes

ECE 667 Synthesis & Verification - BDD 17 Restrict Operator ( ) Restrict Operator ( f (c=0, d=1) ) f = (a+d)(b+c)+a’d’bc Set c = 0Set d = 1 10 a d b c b c 10 a d b b c 10 a b b c d f c’ = (a+d)b Restricted BDD f c’d = (a+1)b = b 1 b 0 f c’d = b Original BDD

ECE 667 Synthesis & Verification - BDD 18 Useful BDD Operators – Apply Operation Basic operator for efficient BDD manipulation (structural) Based on recursive Shannon expansion F G = x (F x G x ) + x’(F x’ G x’ ) where = binary operations: OR, AND, XOR, etc

ECE 667 Synthesis & Verification - BDD 19 APPLY Operator Useful in constructing BDD for arbitrary Boolean logic Any logic operation can be expressed using Apply (ITE) Efficient algorithms, work directly on BDD graphs Apply: F G, any Boolean operationApply: F G, any Boolean operation (AND, OR, XOR,  ) =  F G F G 

ECE 667 Synthesis & Verification - BDD 20 Apply Operation (cont’d) Apply: F GApply: F G where stands for any Boolean operator (AND, OR, XOR, etc)   =  F G F G  Any logic operation can be expressed using only Restrict and Apply Efficient algorithms, work directly on BDDs Apply can be used to construct a BDD bottom-up From primary inputs, through internal logic gates, to output Apply: F GApply: F G where stands for any Boolean operator (AND, OR, XOR, etc)

ECE 667 Synthesis & Verification - BDD 21 Apply Operation - AND 10 a c ac a AND c 10 a 2 c 303 2.3 a c 1.3 11111010 AND = = F  G = x (F x  G x ) + x’(F x’  G x’ )

ECE 667 Synthesis & Verification - BDD 22 Apply Operation - OR OR ac 10 a c 4 5 bc 10 b c 6 7 = = 10 a b c f = ac+bc c a b F + G = x (F x + G x ) + x’(F x’ +G x’ )

ECE 667 Synthesis & Verification - BDD 23 Application to Verification Equivalence Checking of combinational circuits Canonicity property of BDDs: – –if F and G are equivalent, their BDDs are identical (for the same ordering of variables ) 10 a b c F = a’bc + abc +ab’c G = ac +bc 10 a b c 

ECE 667 Synthesis & Verification - BDD 24 Application to SAT Functional test generation – –SAT, Boolean satisfiability analysis – –to test for H = 1 (0), find a path in the BDD to terminal 1 (0) – –the path, expressed in function variables, gives a satisfying solution (test vector) ab ab’c H 0 1 a b c Problem: size explosion

ECE 667 Synthesis & Verification - BDD 25 Efficient Implementation of BDD’s Unique Table: key = (v,G,H), where F = ITE(v,G,H). avoids duplication of existing nodes – –Hash-Table: hash-function(key) = value – –identical to the use of a hash-table in AND/INVERTER circuits hash value of key collision chain hash value of key No collision chain Computed Table: key = (F,G,H) avoids re-computation of existing results

ECE 667 Synthesis & Verification - BDD 26 Unique Table - Hash Table Before a node (v, g, h ) is added to BDD data base, it is looked up in the “unique-table”. If it is there, then existing pointer to node is used to represent the logic function. Otherwise, a new node is added to the unique-table and the new pointer returned. Thus a strong canonical form is maintained. The node for f = (v, g, h ) exists iff(v, g, h ) is in the unique-table. There is only one pointer for (v, g, h ) and that is the address to the unique-table entry. Unique-table allows single multi-rooted DAG to represent all users’ functions: hash index of key collision chain

ECE 667 Synthesis & Verification - BDD 27 Computed Table Keep a record of (F, G, H ) triplets already computed by the ITE operator – –software cache ( “cache” table) – –simply hash-table without collision chain (lossy cache)

ECE 667 Synthesis & Verification - BDD 28 Extension - Complement Edges Combine inverted functions by using complemented edge – –similar to circuit case – –reduces memory requirements – –BUT MORE IMPORTANT: makes some operations more efficient (NOT, ITE) 01 G GG only one DAG using complement pointer 01 G 01 GG two different DAGs

ECE 667 Synthesis & Verification - BDD 29 Extension - Complement Edges To maintain strong canonical form, need to resolve 4 equivalences: VV VV VV VV Solution: Always choose one on left, i.e. the “then” leg must have no complement edge.