Class uvm_pkg::uvm_random_stimulus
Collaboration Diagram of uvm_random_stimulus
Name |
Default value |
Description |
---|---|---|
T |
uvm_transaction |
Name |
Type |
Description |
---|---|---|
type_name |
string |
|
blocking_put_port |
Port blocking_put_port The blocking_put_port is used to send the generated stimulus to the rest of the testbench. |
Name |
Actual Type |
Description |
---|---|---|
this_type |
Constructors
- new(string name, uvm_component parent)
Function
new
Creates a new instance of a specialization of this class. Also, displays the random state obtained from a get_randstate call. In subsequent simulations, set_randstate can be called with the same value to reproduce the same sequence of transactions.
- Parameters:
name (string)
parent (uvm_component)
Functions
- stop_stimulus_generation()
Function
stop_stimulus_generation
Stops the generation of stimulus. If a subclass of this method has forked additional processes, those processes will also need to be stopped in an overridden version of this method
- get_type_name()
Tasks
- generate_stimulus(uvm_transaction t = null, int max_count = 0)
Function
generate_stimulus
Generate up to max_count transactions of type T. If t is not specified, a default instance of T is allocated and used. If t is specified, that transaction is used when randomizing. It must be a subclass of T.
max_count is the maximum number of transactions to be
-
generated. A value of zero indicates no maximum
-
in this case, generate_stimulus will go on indefinitely unless stopped by some other process
- Parameters:
t (uvm_transaction)
max_count (int)
-
CLASS
uvm_random_stimulus #(T)
A general purpose unidirectional random stimulus class.
The uvm_random_stimulus class generates streams of T transactions. These streams may be generated by the randomize method of T, or the randomize method of one of its subclasses. The stream may go indefinitely, until terminated by a call to stop_stimulus_generation, or we may specify the maximum number of transactions to be generated.
By using inheritance, we can add directed initialization or tidy up after random stimulus generation. Simply extend the class and define the run task, calling super.run() when you want to begin the random stimulus phase of simulation.
While very useful in its own right, this component can also be used as a template for defining other stimulus generators, or it can be extended to add additional stimulus generation methods and to simplify test writing.