What’s New in MyHDL 0.3

Author:Jan Decaluwe

VCD output for waveform viewing

image

MyHDL now has support for waveform viewing. During simulation, signal changes can be written to a VCD output file that can be loaded into a waveform viewer tool such as gtkwave.

The user interface of this feature consists of a single function, traceSignals(). To explain how it works, recall that in MyHDL, an instance is created by assigning the result of a function call to an instance name. For example:

tb_fsm = testbench()

To enable VCD tracing, the instance should be created as follows instead:

tb_fsm = traceSignals(testbench)

All signals in the instance hierarchy will be traced in a VCD file called . Note that first the argument of traceSignals() consists of the uncalled function. By calling the function under its control, traceSignals() gathers information about the hierarchy and the signals to be traced. In addition to a function argument, traceSignals() accepts an arbitrary number of non-keyword and keyword arguments that will be passed to the function call.

Signals are dumped in a suitable format. This format is inferred at the Signal construction time, from the type of the initial value. In particular, bool signals are dumped as single bits. (This only works starting with Python 2.3, when bool has become a separate type). Likewise, intbv signals with a defined bit width are dumped as bit vectors. To support the general case, other types of signals are dumped as a string representation, as returned by the standard str() function.

[warning] Support for literal string representations is not part of the VCD standard. It is specific to gtkwave. To generate a standard VCD file, you need to use signals with a defined bit width only.

Enumeration types

It is often desirable to define a set of identifiers. A standard Python idiom for this purpose is to assign a range of integers to a tuple of identifiers, like so:

>>> SEARCH, CONFIRM, SYNC = range(3)
>>> CONFIRM
1

However, this technique has some drawbacks. Though it is clearly the intention that the identifiers belong together, this information is lost as soon as they are defined. Also, the identifiers evaluate to integers, whereas a string representation of the identifiers would be preferable. To solve these issues, we need an enumeration type.

MyHDL 0.3 supports enumeration types by providing a function enum(). The arguments to enum() are the string representations of the identifiers, and its return value is an enumeration type. The identifiers are available as attributes of the type. For example:

>>> from myhdl import enum
>>> t_State = enum('SEARCH', 'CONFIRM', 'SYNC')
>>> t_State
<Enum: SEARCH, CONFIRM, SYNC>
>>> t_State.CONFIRM
CONFIRM

Enumeration types are often used for the state variable in a finite state machine. In the waveform in Section 1, you see a Signal called state. Note how the waveforms show the string representation of the enumeration type identifiers The state signal has been constructed with an enumeration type identifier as its initial value, as follows:

state = Signal(t_State.SEARCH)

Inferring the sensitivity list for combinatorial logic

In MyHDL, combinatorial logic is described by a generator function with a sensitivity list that contains all inputs signals (the signals that are read inside the function).

It may be easy to forget some input signals, especially it there are a lot of them or if the code is being modified. There are various ways to solve this. One way is to use a sophisticated editor. Another way is direct language support. For example, recent versions of Verilog have the always @* construct, that infers all input signals. The SystemVerilog 3.1 standard improves on this by introducing the always_comb block with slightly enhanced semantics.

MyHDL 0.3 provides a function called always_comb() which is named and modeled after the SystemVerilog counterpart. always_comb() takes a classic local function as its argument. This function should specify the combinatorial logic behavior. always_comb() returns a generator that is sensitive to all inputs, and that will run the function whenever an input changes.

For example, suppose that we have a mux module described as follows:

def mux(z, a, b, sel):
    """ Multiplexer.

    z -- mux output
    a, b -- data inputs
    sel -- control input

    """
    def logic()
        while 1:
            yield a, b, sel
            if sel == 1:
                z.next = a
            else:
                z.next = b
    mux_logic = logic()
    return mux_logic

Using always_comb(), we can describe it as follows instead:

def mux(z, a, b, sel):
    """ Multiplexer.

    z -- mux output
    a, b -- data inputs
    sel -- control input

    """
    def logic()
        if sel == 1:
            z.next = a
        else:
            z.next = b
    mux_logic = always_comb(logic)
    return mux_logic

Note that in the first version, the sensitivity list is at the beginning of the generator function code. This is traditionally done in synthesizable RTL style modeling. However, the semantics of this style are not entirely correct: at the start of the simulation, the combinatorial output will not reflect the initial state of the inputs. always_comb() solves this by putting the sensitivity list at the end of the code.

Inferring the list of instances

In MyHDL, the instances defined in a top level function need to be returned explicitly. The following is a schematic example:

def top(...):
    ...
    instance_1 = module_1(...)
    instance_2 = module_2(...)
    ...
    instance_n = module_n(...)
    ...
    return instance_1, instance_2, ... , instance_n

It may be convenient to assemble the list of instances automatically, especially if there are many instances. For this purpose, MyHDL 0.3 provides the function instances(). It is used as follows:

from myhdl import instances

def top(...):
    ...
    instance_1 = module_1(...)
    instance_2 = module_2(...)
    ...
    instance_n = module_n(...)
    ...
    return instances()

Function instances() uses introspection to inspect the type of the local variables defined by the calling function. All variables that comply with the definition of an instance are assembled in a list, and that list is returned.

Inferring the list of processes

In addition to instances, a top level function may also define local generators functions, which I will call processes because of the analogy with VHDL. Like instances, processes need to be returned explicitly, with the qualification that they have to be called first to turn them into generators. The following is a schematic example:

def top(...):
    ...
    def process_1():
        ...
    def process_2():
        ...
    ...
    def process_n():
        ...
    ...
    return process_1(), process_2(), ..., process_n()

As for instances, it may be more convenient to assemble the list of processes automatically. One option is to turn each process into an instance by calling it and assigning the returned generator to a local variable. Those instances will then be found by the instances() function described in Section 4.

Another option is to use the function processes() provided by MyHDL 0.3. This function uses introspection to find the processes, calls each of them, and assembles the returned generators into a list. It can be used as follows:

from myhdl import processes

def top(...):
    ...
    def process_1():
        ...
    def process_2():
        ...
    ...
    def process_n():
        ...
    ...
    return processes()

To conclude, a top level function with both instances and processes can use the following idiomatic code to return all of them:

return instances(), processes()

Class intbv enhancements

Class intbv has been enhanced with new features.

It is now possible to leave the left index of a slicing operation unspecified. The meaning is to access “all” higher order bits. For example:

>>> from myhdl import intbv
>>> n = intbv()
>>> hex(n)
'0x0'
>>> n[:] = 0xde
>>> hex(n)
'0xde'
>>> n[:8] = 0xfa
>>> hex(n)
'0xfade'
>>> n[8:] = 0xb4
>>> hex(n)
'0xfab4'

intbv objects now have min and max attributes that can be specified at construction time. The meaning is that only values within range(min, max) are permitted. The default value for these attributes is None, meaning “infinite”. For example (traceback output shortened for clarity):

>>> n = intbv(min=-17, max=53)
>>> n
intbv(0)
>>> n.min
-17
>>> n.max
53
>>> n[:] = 28
>>> n
intbv(28)
>>> n[:] = -18
Traceback (most recent call last):
    ....
ValueError: intbv value -18 < minimum -17
>>> n[:] = 53
Traceback (most recent call last):
    ....
ValueError: intbv value 53 >= maximum 53

When a slice is taken from an intbv object, the return value is a new intbv object with a defined bit width. As in Verilog, the value of the new intbv object is always positive, regardless of the sign of the original value. In addition, the min and max attributes are set implicitly:

>>> v = intbv()[6:]
>>> v
intbv(0)
>>> v.min
0
>>> v.max
64

Lastly, a small change was implemented with regard to binary operations. In previous versions, both numeric and bit-wise operations always returned a new intbv object, even in mixed-mode operations with int objects. This has changed: numeric operations return an int, and bitwise operations return a intbv. In this way, the return value corresponds better to the nature of the operation.

Function concat()

In previous versions, the intbv class provided a method. This method is no longer available. Instead, there is now a concat() function that supports a much broader range of objects.

A function is more natural because MyHDL objects of various types can be concatenated: intbv objects with a defined bit width, bool objects, the corresponding signal objects, and bit strings. All these objects have a defined bit width. Moreover, the first argument doesn’t need to have a defined bit width. It can also be an unsized intbv, an int, a long, or a corresponding signal object. Function concat() returns an intbv object.

Python 2.3 support

Python 2.3 was released on July 29, 2003, and as of this writing, it is the latest stable Python release. MyHDL 0.3 works with both Python 2.2 and Python 2.3. In good Python tradition, MyHDL code developed with Python 2.2 should run without changes or problems in Python 2.3.

In general, I am not that keen on early upgrading. However, as it happens, the evolution of Python enables features that are really important or even crucial to MyHDL. Python 2.2 generators are the best example: they are the cornerstone of MyHDL. But Python 2.3 also has significant benefits, which I will summarize below.

First, generators and the yield statement are a default Python 2.3 feature. This means that statements are no longer required.

Second, Python 2.3 has a bool type, which is implemented as a subtype of int. For general Python use, the implications are rather limited - the main difference is that logical result values will print as False and True instead of 0 and 1. However, in MyHDL, I can use the bool type to infer a bit width. If a Signal is constructed with a bool value, it is a single bit Signal. One application is waveform viewing as in Section 1 In the waveform, note how single bit signals are displayed as level changes. With Python 2.2, the waveforms of these signals would only show value changes, which is not as clear for single bits.

Finally, Python 2.3 is significantly faster. MyHDL code runs 25–35% faster in Python 2.3. This is a very nice speedup compared to the small burden of a straightforward upgrade.

Python is a very stable language, so upgrading to Python 2.3 is virtually risk free. Given the additional benefits, I recommend MyHDL users to do so as soon as possible. For the next major MyHDLrelease, the new features will become required and only Python 2.3 (and higher) will be supported.