Now let's take a look at some of the common ways of writing constraint expressions inside a constraint block.

## Simple expressions

Note that there can be only one relational operator < <= > >= in an expression.

class MyClass;
rand bit [7:0] min, typ, max;

// Valid expression
constraint my_range { 0 < min;
typ < max;
typ > min;
max < 128; }

// Use of multiple operators in a single expression is not allowed
constraint my_error { 0 < min < typ < max < 128; }

// This will set min to 16 and randomize all others
constraint my_min {  min == 16; }

// This will set max to a random value greater than or equal to 64
constraint my_max {  max >= 64; }
endclass

You cannot make assignments inside a constraint block as it only contains expressions. Instead you have to use an equivalence operator == as shown for the constraint named my_min in the example above where min will get a value of 16 and all other variables will be randomized. This is one way of fixing a particular value to a variable even if the solver attempts to randomize it.

constraint my_min { min == temp.low * 9/5 + 32; }

You can also use a little more complex expression like the one shown above where min represents a variable to store Fahrenheit and low is a variable within the class object temp that holds temperature in Celsius.

Example

class myClass;
rand bit [3:0] min, typ, max;
rand bit [3:0] fixed;

constraint my_range { 3 < min;
typ < max;
typ > min;
max < 14; }

constraint c_fixed { fixed == 5; }

function string display ();
return \$sformatf ("min=%0d typ=%0d max=%0d fixed=%d", min, typ, max, fixed);
endfunction

endclass

module tb;
initial begin
for (int i = 0; i < 10; i++) begin
myClass cls = new ();
cls.randomize();
\$display ("itr=%0d %s", i, cls.display());
end
end
endmodule

Note that fixed variable is fixed to the value 5 while other variables have been randomized with values that meet all constraints.

Simulation Log
run -all;
# KERNEL: itr=0 min=7 typ=9 max=12 fixed= 5
# KERNEL: itr=1 min=4 typ=9 max=12 fixed= 5
# KERNEL: itr=2 min=7 typ=10 max=13 fixed= 5
# KERNEL: itr=3 min=4 typ=6 max=11 fixed= 5
# KERNEL: itr=4 min=8 typ=12 max=13 fixed= 5
# KERNEL: itr=5 min=5 typ=6 max=13 fixed= 5
# KERNEL: itr=6 min=6 typ=9 max=13 fixed= 5
# KERNEL: itr=7 min=10 typ=12 max=13 fixed= 5
# KERNEL: itr=8 min=5 typ=11 max=13 fixed= 5
# KERNEL: itr=9 min=6 typ=9 max=11 fixed= 5
# KERNEL: Simulation has finished. There are no more test vectors to simulate.
exit

## inside operator

You can specify a lower and an upper limit as an alternative to the expression shown below using an inside operator.

constraint my_range { typ > 32;
typ < 256; }

// typ >= 32 and typ <= 256
constraint new_range { typ inside {[32:256]}; }

// Choose from the following values
constraint spec_range { type inside {32, 64, 128}; }

Note that an inside construct includes both lower and upper limits. SystemVerilog gathers all the values and chooses between the values with equal probability unless there are other constraints on the variable.

## Inverted inside operator

If you want any value outside a specific range, an inverted constraint can be written as follows. This will produce a random value from 0 to 31 since typ is an 8-bit variable and the upper limit already covers the maximum value it can hold.

rand bit [2:0] typ;
constraint inv_range { ! (typ inside {[3:6]}); }

Note that repeated randomization gave all values except the ones that fall within the range 3 through 6.

Simulation Log
run -all;
# KERNEL: itr=0 typ=7
# KERNEL: itr=1 typ=0
# KERNEL: itr=2 typ=7
# KERNEL: itr=3 typ=0
# KERNEL: itr=4 typ=0
# KERNEL: itr=5 typ=0
# KERNEL: itr=6 typ=7
# KERNEL: itr=7 typ=1
# KERNEL: itr=8 typ=1
# KERNEL: itr=9 typ=7
# KERNEL: Simulation has finished. There are no more test vectors to simulate.
exit

## Weighted distributions

The dist operator allows you to create weighted distributions so that some values are chosen more often than others. The := operator specifies that the weight is the same for every specified value in the range while the :/ operator specifies that the weight is to be equally divided between all the values.

:= operator

rand bit [2:0] typ;
constraint dist1 	{  typ dist { 0:=20, [1:5]:=50, 6:=40, 7:=10}; }

In dist1, the weight of 0 is 20, 6 is 40, and 7 is 10 while 1 through 5 is 50, for a total of 320. Hence the probability of choosing 0 is 20/320 and the probability of choosing a value between 1 and 5 is 50/320. Let's look at a simple example.

class myClass;
rand bit [2:0] typ;
constraint dist1 	{  typ dist { 0:=20, [1:5]:=50, 6:=40, 7:=10}; }
endclass

module tb;
initial begin
for (int i = 0; i < 10; i++) begin
myClass cls = new ();
cls.randomize();
\$display ("itr=%0d typ=%0d", i, cls.typ);
end
end
endmodule

In the log shown below, note that 1 through 5 has appeared more than 0, 6 or 7 because they have a higher weight and are chosen more often.

Simulation Log
run -all;
# KERNEL: itr=0 typ=5
# KERNEL: itr=1 typ=1
# KERNEL: itr=2 typ=6
# KERNEL: itr=3 typ=3
# KERNEL: itr=4 typ=2
# KERNEL: itr=5 typ=3
# KERNEL: itr=6 typ=0
# KERNEL: itr=7 typ=5
# KERNEL: itr=8 typ=1
# KERNEL: itr=9 typ=4
# KERNEL: Simulation has finished. There are no more test vectors to simulate.
exit
:/ operator

rand bit [2:0] typ;
constraint dist2  	{  typ dist { 0:/20, [1:5]:/50, 6:/10, 7:/20}; }

In dist2, the weight of 0 is 20, 6 is 10 and 7 is 20 while 1 through 5 share a total weight of 50, thus have 10 each. Hence the probability of choosing 0 is 20/100, and the probability of choosing a value between 1 and 5 is 10/100. Let's look at a simple example.

class myClass;
rand bit [2:0] typ;
constraint dist2 	{  typ dist { 0:/20, [1:5]:/50, 6:/40, 7:/10}; }
endclass

module tb;
initial begin
for (int i = 0; i < 10; i++) begin
myClass cls = new ();
cls.randomize();
\$display ("itr=%0d typ=%0d", i, cls.typ);
end
end
endmodule

In the log shown below, note that 6 has appeared more than the others because it has the highest weight.

Simulation Log
run -all;
# KERNEL: itr=0 typ=5
# KERNEL: itr=1 typ=6
# KERNEL: itr=2 typ=4
# KERNEL: itr=3 typ=6
# KERNEL: itr=4 typ=6
# KERNEL: itr=5 typ=4
# KERNEL: itr=6 typ=2
# KERNEL: itr=7 typ=3
# KERNEL: itr=8 typ=4
# KERNEL: itr=9 typ=6
# KERNEL: Simulation has finished. There are no more test vectors to simulate.
exit

## Bidirectional constraints

Constraint blocks are not executed from top to bottom like procedural code, but are all active at the same time. Let's see this with another example.

class myClass;
rand bit [3:0] val;
constraint  c1 { val > 3;
val < 12; }

constraint  c2  {val >= 10; }
endclass

module tb;
initial begin
for (int i = 0; i < 10; i++) begin
myClass cls = new ();
cls.randomize();
\$display ("itr=%0d typ=%0d", i, cls.val);
end
end
endmodule

Note that constraints c1 and c2 limits the values to 10 and 11.

Simulation Log
run -all;
# KERNEL: itr=0 typ=11
# KERNEL: itr=1 typ=11
# KERNEL: itr=2 typ=11
# KERNEL: itr=3 typ=10
# KERNEL: itr=4 typ=11
# KERNEL: itr=5 typ=10
# KERNEL: itr=6 typ=10
# KERNEL: itr=7 typ=10
# KERNEL: itr=8 typ=10
# KERNEL: itr=9 typ=10
# KERNEL: Simulation has finished. There are no more test vectors to simulate.
exit