\chapter{Introduction to \myhdl\ }

\section{A basic \myhdl\ simulation}

We will introduce \myhdl\ with a classical \code{Hello World} style
example. Here are the contents of a \myhdl\ simulation script called
\file{hello1.py}:

\begin{verbatim}
from myhdl import delay, now, Simulation

def sayHello():
    while 1:
        yield delay(10)
        print "%s Hello World!" % now()

gen = sayHello()
sim = Simulation(gen)
sim.run(30)

\end{verbatim}

When we run this simulation, we get the following output: 

\begin{verbatim}
% python hello1.py
10 Hello World!
20 Hello World!
30 Hello World!
StopSimulation: Simulated for duration 30

\end{verbatim}

The first line of the script imports a
number of objects from the \code{myhdl} package. In good Python style, and
unlike most other languages, we can only use identifiers that are
\emph{literally} defined in the source file \footnote{I don't want to
explain the \samp{import *} syntax}. 

Next, we define a generator function called
\code{sayHello}. This is a generator function (as opposed to
a classic Python function) because it contains a \keyword{yield}
statement (instead of \keyword{return} statement). In \myhdl\, a
\keyword{yield} statement has a similar purpose as a \keyword{wait}
statement in VHDL: the statement suspends execution of the function,
and its clauses specify when the function should resume. In this case,
there is a \code{delay} clause, that specifies the required delay.

To make sure that the generator runs ``forever'', we wrap its behavior
in a \code{while 1} loop. This is a standard Python idiom, and it is
the \myhdl\ equivalent of the implicit looping behavior of a Verilog
\keyword{always} block and a VHDL \keyword{process}.

In \myhdl\, the basic simulation objects are generators. Generators
are created by calling generator functions. For example, variable
\code{gen} refers to a generator. To simulate this generator, we pass
it as an argument to a \class{Simulation} object constructor.  We then
run the simulation for the desired amount of time. In \myhdl\, time
is modeled by a natural integer.


\section{Concurrent generators and signals}

In the previous section, we simulated a single generator. Of course,
real hardware descriptions are not like that: in fact, they are
typically massively concurrent. \myhdl\ supports this by allowing an
arbitrary number of concurrent generators. More specifically, a
\class{Simulation} constructor can take an arbitrary number of
arguments, each of which can be a generator or a nested sequence of
generators.

With concurrency comes the problem of deterministic
communication. Therefore, hardware languages use special objects to
support deterministic communication between concurrent code. \myhdl\
has as \class{Signal} object which is roughly modeled after VHDL
signals.

We will demonstrate these concepts by extending and modifying our
first example. We introduce a clock signal, driven by a second
generator: 

\begin{verbatim}
clk = Signal(0)

def clkGen():
    while 1:
        yield delay(10)
        clk.next = 1
        yield delay(10)
        clk.next = 0

\end{verbatim}

The \code{clk} signal is constructed with an initial value
\code{0}. In the clock generator function \code{clkGen}, it is
continuously assigned a new value after a certain delay. In \myhdl{},
the new value of a signal is specified by assigning to its
\code{next} attribute. This is the \myhdl\ equivalent of VHDL signal
assignments and Verilog's non-blocking assignments.

The \code{sayHello} generator function is modified to wait for a
rising edge of the clock instead of a delay:

\begin{verbatim}
def sayHello():
    while 1:
        yield posedge(clk)
        print "%s Hello World!" % now()

\end{verbatim}

Waiting for a clock edge is achieved with a second form of the
\keyword{yield} statement: \samp{yield posedge(\var{signal})}. 
A \class{Simulation} object will suspend the generator as that point,
and resume it when there is a rising edge on the signal.

The \class{Simulation} is now constructed with 2 generator arguments:

\begin{verbatim}
sim = Simulation(clkGen(), sayHello())
sim.run(50)

\end{verbatim}

When we run this simulation, we get:

\begin{verbatim}
% python hello2.py
10 Hello World!
30 Hello World!
50 Hello World!
StopSimulation: Simulated for duration 50

\end{verbatim}


\section{Parameters, instantiations and hierarchy}

So far, the generator function examples had no parameters. For
example, the \code{clk} signal was defined in the enclosing scope of
the generator functions. However, to make the code reusable we will
want to pass arguments through a parameter list. For example, we can
change the clock generator function to make it more general
and reusable, as follows:

\begin{verbatim}
def clkGen(clock, period=20):
    lowTime = int(period / 2)
    highTime = period - lowTime
    while 1:
        yield delay(lowTime)
        clock.next = 1
        yield delay(highTime)
        clock.next = 0

\end{verbatim}

The clock signal is now a parameter of the function. Also, the clock
\var{period} is a parameter with a default value of \code{20}.
This makes \var{period} an \dfn{optional} parameter; if it is not
specified in a call, the default value will be used.

Similarly, the \code{sayHello} function can be made more general:

\begin{verbatim}
def sayHello(clock, to="World!"):
    while 1:
        yield posedge(clock)
        print "%s Hello %s" % (now(), to)

\end{verbatim}

We can create any number of generators by calling generator functions
with the appropriate parameters. This is very similar to the concept of
\dfn{instantiation} in hardware description languages and we will use
the same terminology in \myhdl{}. Hierarchy can be modeled by defining
the instances in a higher-level function, and returning them. For
example: 

\begin{verbatim}
def greetings():

    clk1 = Signal(0)
    clk2 = Signal(0)
    
    clkGen1 = clkGen(clk1)
    clkGen2 = clkGen(clock=clk2, period=19)
    sayHello1 = sayHello(clock=clk1)
    sayHello2 = sayHello(to="MyHDL", clock=clk2)

    return clkGen1, clkGen2, sayHello1, sayHello2

\end{verbatim}
As in standard Python, positional or named parameter association can
be used in instantiations, or a mix of both\footnote{All positional
parameters have to come before any named parameter.}. All these styles
are demonstrated in the example above. As in hardware description
languages, named association can be very useful if there are a lot of
parameters, as the parameter order in the call does not matter in that
case.

\class{Simulation} constructor arguments can also be sequences of
generators. In this way, they support hierarchy: the return value of a
higher-level instantiating function can directly be used an
argument. For example:

\begin{verbatim}
sim = Simulation(greetings())
sim.run(50)

\end{verbatim}

This produces the following output:

\begin{verbatim}
% python greetings.py
9 Hello MyHDL
10 Hello World!
28 Hello MyHDL
30 Hello World!
47 Hello MyHDL
50 Hello World!
StopSimulation: Simulated for duration 50

\end{verbatim}


\section{Bit-oriented operations}

Hardware design involves dealing with bits and bit-oriented
operations. The standard Python type \class{int} has most of the
desired features, but lacks support for indexing and
slicing. Therefore,
\myhdl\ provides the \class{intbv} class. The name was chosen to
suggest an integer with bit vector flavour.

Class \class{intbv} works transparently as an integer and with other
integer-like types. Like class \class{int}, it provides access to the
underlying two's complement representation for bitwise
operations. In addition, it is a mutable type that provides indexing
and slicing operations, and some additional bit-oriented support such
as concatenation.

\subsection{Indexing operations}
\label{gray}
As an example, we will consider the design of a Gray encoder. The
following code is a Gray encoder modeled in \myhdl{}:

\begin{verbatim}
def bin2gray(B, G, width):
    """ Gray encoder.

    B -- input intbv signal, binary encoded
    G -- output intbv signal, Gray encoded
    width -- bit width
    """
    while 1:
        yield B
        for i in range(width):
            G.next[i] = B[i+1] ^ B[i]

\end{verbatim}

This code introduces a few new concepts. The string in triple quotes
at the start of the function is a \dfn{doc string}. This is standard
Python practice for structured documentation of code. Moreover, we
use a third form of the \keyword{yield} statement:
\samp{yield \var{signal}}. This specifies that the generator should
resume whenever \var{signal} changes value. This is typically used to
describe combinatorial logic.
Finally, the code contains bit indexing operations and an exclusive-or
operator as required for a Gray encoder. By convention, the lsb of an
\class{intbv} object has index \code{0}.

To verify the Gray encoder, we write a test bench that prints input
and output for all possible input values:

\begin{verbatim}
def testBench(width):
    
    B = Signal(intbv(0))
    G = Signal(intbv(0))
    
    dut = bin2gray(B, G, width)

    def stimulus():
        for i in range(2**width):
            B.next = intbv(i)
            yield delay(10)
            print "B: " + bin(B, width) + "| G: " + bin(G, width)

    return (dut, stimulus())

\end{verbatim}

We use the conversion function \code{bin} to get a binary
string representation of the signal values. This function is exported
by the \code{myhdl} package and complements the standard Python
\code{hex} and \code{oct} conversion functions.

To demonstrate, we set up a simulation for a small width: 

\begin{verbatim}
Simulation(testBench(width=3)).run()

\end{verbatim}

The simulation produces the following output:

\begin{verbatim}
% python bin2gray.py
B: 000 | G: 000
B: 001 | G: 001
B: 010 | G: 011
B: 011 | G: 010
B: 100 | G: 110
B: 101 | G: 111
B: 110 | G: 101
B: 111 | G: 100
StopSimulation: No more events

\end{verbatim}

\subsection{Slicing operations}

For a change, we will use a plain function as an example to illustrate
slicing.  The following function calculates the HEC byte of an ATM
header.

\begin{verbatim}
from myhdl import intbv
concat = intbv.concat # shorthand alias

COSET = 0x55

def calculateHec(header):
    """ Return hec for an ATM header, represented as an intbv.

    The hec polynomial is 1 + x + x**2 + x**8.
    """
    hec = intbv(0)
    for bit in header[32:]:
        hec[8:] = concat(hec[7:2],
                         bit ^ hec[1] ^ hec[7],
                         bit ^ hec[0] ^ hec[7],
                         bit ^ hec[7]
                        )
    return hec ^ COSET

\end{verbatim}

The code shows how slicing access and assignment is supported on the
\class{intbv} data type. In accordance with the most common hardware
convention, and unlike standard Python, slicing ranges are
downward. The code also demonstrates concatenation of \class{intbv}
objects.

As in standard Python, the slicing range is half-open: the highest
index bit is not included. Unlike standard Python however, this index
corresponds to the \emph{leftmost} item. The rightmost index is
\code{0} by default, and can be ommitted from the slice.

The half-openness of a slice may seem awkward at first, but it helps
to avoid one-off count issues in practice. For example, the slice
\code{hex[8:]} has exactly \code{8} bits. Likewise, the slice
\code{hex[7:2]} has \code{7-2=5} bits. You can think about it as
follows: for a slice \code{[i:j]}, only bits below index \code{i} are
included, and the bit with index \code{j} is the last bit included.