# Binary Decision Diagrams¶

A binary decision diagram is a directed acyclic graph used to represent a Boolean function. They were originally introduced by Lee [1], and later by Akers [2]. In 1986, Randal Bryant introduced the reduced, ordered BDD (ROBDD) [3].

The ROBDD is a canonical form, which means that given an identical ordering of input variables, equivalent Boolean functions will always reduce to the same ROBDD. This is a very desirable property for determining formal equivalence. Also, it means that unsatisfiable functions will be reduced to zero, making SAT/UNSAT calculations trivial. Due to these auspicious properties, the term BDD almost always refers to some minor variation of the ROBDD devised by Bryant.

In this chapter, we will discuss how to construct and visualize ROBDDs using PyEDA.

The code examples in this chapter assume that you have already prepared your terminal by importing all interactive symbols from PyEDA:

>>> from pyeda.inter import *


## Constructing BDDs¶

There are two ways to construct a BDD:

1. Convert an expression
2. Use operators on existing BDDs

### Convert an Expression¶

Since the Boolean expression is PyEDA’s central data type, you can use the expr2bdd function to convert arbitrary expressions to BDDs:

>>> f = expr("a & b | a & c | b & c")
>>> f
Or(And(a, b), And(a, c), And(b, c))
>>> f = expr2bdd(f)
>>> f
<pyeda.boolalg.bdd.BinaryDecisionDiagram at 0x7f556874ed68>


As you can see, the BDD does not have such a useful string representation. More on this subject later.

### Using Operators¶

Just like expressions, BDDs have their own Variable implementation. You can use the bddvar function to construct them:

>>> a, b, c = map(bddvar, 'abc')
>>> type(a)
pyeda.boolalg.bdd.BDDVariable
>>> isinstance(a, BinaryDecisionDiagram)
True


Creating indexed variables with namespaces always works just like expressions:

>>> a0 = bddvar('a', 0)
>>> a0
a[0]
>>> b_a_0_1 = bddvar(('a', 'b'), (0, 1))
b.a[0,1]


Also, the bddvars function can be used to create multi-dimensional arrays of indexed variables:

>>> X = bddvars('x', 4, 4)
>>> X
farray([[x[0,0], x[0,1], x[0,2], x[0,3]],
[x[1,0], x[1,1], x[1,2], x[1,3]],
[x[2,0], x[2,1], x[2,2], x[2,3]],
[x[3,0], x[3,1], x[3,2], x[3,3]]])


From variables, you can use Python’s ~|&^ operators to construct arbitrarily large BDDs.

Here is the simple majority function again:

>>> f = a & b | a & c | b & c
>>> f
<pyeda.boolalg.bdd.BinaryDecisionDiagram at 0x7f556874ed68>


This time, we can see the benefit of having variables available:

>>> f.restrict({a: 0})
<pyeda.boolalg.bdd.BinaryDecisionDiagram at 0x7f556874eb38>
>>> f.restrict({a: 1, b: 0})
c
>>> f.restrict({a: 1, b: 1})
1


## BDD Visualization with IPython and GraphViz¶

If you have GraphViz installed on your machine, and the dot executable is available on your shell’s path, you can use the gvmagic IPython extension to visualize BDDs.

In [1]: %install_ext https://raw.github.com/cjdrake/ipython-magic/master/gvmagic.py



For example, here is the majority function in three variables as a BDD:

In [3]: a, b, c = map(bddvar, 'abc')

In [4]: f = a & b | a & c | b & c

In [5]: %dotobj f


BDD of Three-Input Majority Function

The way this works is that the %dotobj extension internally calls the to_dot method on f:

In [6]: f.to_dot()
'graph BDD {
n139865543613912 [label=0,shape=box];
n139865543728208 [label=1,shape=box];
n139865543169752 [label="c",shape=circle];
n139865552542296 [label="b",shape=circle];
n139865543169864 [label="b",shape=circle];
n139865543170312 [label="a",shape=circle];
n139865543169752 -- n139865543613912 [label=0,style=dashed];
n139865543169752 -- n139865543728208 [label=1];
n139865552542296 -- n139865543613912 [label=0,style=dashed];
n139865552542296 -- n139865543169752 [label=1];
n139865543169864 -- n139865543169752 [label=0,style=dashed];
n139865543169864 -- n139865543728208 [label=1];
n139865543170312 -- n139865552542296 [label=0,style=dashed];
n139865543170312 -- n139865543169864 [label=1]; }'


## Satisfiability¶

Like we mentioned in the introduction, BDDs are a canonical form. That means that all unsatisfiable functions will reduce to zero, and all tautologies will reduce to one. If you simply want to check whether a function is SAT or UNSAT, just construct a BDD, and test whether it is zero/one.

>>> a, b = map(bddvar, 'ab')
>>> f = ~a & ~b | ~a & b | a & ~b | a & b
>>> f
1
>>> f.is_one()
True
>>> g = (~a | ~b) & (~a | b) & (a | ~b) & (a | b)
>>> g
0
>>> g.is_zero()
True


If you need one or more satisfying input points, use the satisfy_one and satisfy_all functions. The algorithm that implements SAT is very simple and elegant; it just finds a path from the function’s root node to one.

>>> a, b, c = map(bddvar, 'abc')
>>> f = a ^ b ^ c
>>> f.satisfy_one()
{b: 0, a: 0, c: 1}
>>> list(f.satisfy_all())
[{a: 0, b: 0, c: 1},
{a: 0, b: 1, c: 0},
{a: 1, b: 0, c: 0},
{a: 1, b: 1, c: 1}]


Trace all the paths from the top node to 1 to verify.

BDD of Three-Input XOR Function

## Formal Equivalence¶

Because BDDs are a canonical form, functional equivalence is trivial.

Here is an example where we define the XOR function by using 1) the XOR operator, and 2) OR/AND/NOT operators.

>>> a, b, c = map(bddvar, 'abc')
>>> f1 = a ^ b ^ c
>>> f2 = a & ~b & ~c | ~a & b & ~c | ~a & ~b & c | a & b & c


Just like expressions, BDDs have an equivalent method:

>>> f1.equivalent(f2)
True


However, this isn’t required. PyEDA maintains a unique table of BDD nodes and their function pointers, so you can just test for equality using the Python is operator:

>>> f1 is f2
True


## Variable Ordering¶

The size of a BDD is very sensitive to the order of variable decomposition. For example, here is a BDD that uses an ideal variable order:

In [1]: X = bddvars('x', 8)

In [2]: f1 = X[0] & X[1] | X[2] & X[3] | X[4] & X[5]

In [3]: %dotobj f1


Good Variable Ordering

And here is the same function, with a bad variable order:

In [2]: f2 = X[0] & X[3] | X[1] & X[4] | X[2] & X[5]

In [3]: %dotobj f2


The previous example was used by Bryant [3] to demonstrate this concept. When you think of the definition of a BDD, it becomes clear why some orderings are superior to others. What you want in a variable ordering is to decide as much of the function at every decision level as you traverse towards 0 and 1.

PyEDA implicitly orders all variables. It is therefore not possible to create a new BDD by reordering its inputs. You can, however, rename the variables using the compose method to achieve the desired result.

For example, to optimize the previous BDD:

In [4]: g2 = f2.compose({X[0]: Y[0], X[1]: Y[2], X[2]: Y[4],
X[3]: Y[1], X[4]: Y[3], X[5]: Y[5]})

In [5]: %dotobj g2


After Variable Renaming

## Garbage Collection¶

Since BDDs are a memory-constrained data structure, the subject of garbage collection is very important.

PyEDA uses the Python standard library’s weakref module to automatically garbage collect BDD nodes when they are no longer needed. The BDD function contains a reference to a node, which contains references to it’s children, and so on until you get to zero/one. When a function’s name is either deleted or it goes out of scope, it may initiate a corresponding cascade of node deletions.

This is best illustrated with an example. If you look directly into the pyeda.boolalg.bdd module, you can find the memory structure that holds BDD nodes:

>>> from pyeda.boolalg.bdd import _NODES
>>> len(_NODES)
2


The table contains two static nodes: zero and one. Let’s define a few variables:

>>> from pyeda.inter import *
>>> a, b = map(bddvar, 'ab')
>>> len(_NODES)
4


Now define three simple BDDs:

>>> f1 = a | b
>>> len(_NODES)
5
>>> f2 = a & b
>>> len(_NODES)
6
>>> f3 = a ^ b
>>> len(_NODES)
8


Now there are eight nodes. Let’s count the remaining nodes as we delete functions:

>>> del f1
>>> len(_NODES)
7
>>> del f2
>>> len(_NODES)
6
>>> del f3
>>> len(_NODES)
4


## References¶

 [1] C.Y. Lee, Representation of Switching Circuits by Binary-Decision Programs, Bell System Technical Journal, Vol. 38, July 1959, pp. 985-999.
 [2] S.B. Akers, Binary Decision Diagrams, IEEE Transactions on Computers, Vol. C-27, No. 6, June 1978, pp. 509-516.
 [3] (1, 2) Randal E. Bryant Graph-Based Algorithms for Boolean Function Manipulation, IEEE Transactions on Computers, 1986 http://www.cs.cmu.edu/~bryant/pubdir/ieeetc86.pdf