Hardware design involves dealing with bits and bit-oriented operations. The
standard Python type
int has most of the desired features, but lacks
support for indexing and slicing. For this reason, MyHDL provides the
intbv class. The name was chosen to suggest an integer with bit vector
intbv works transparently with other integer-like types. Like
int, it provides access to the underlying two’s complement
representation for bitwise operations. However, unlike
int, it is
a mutable type. This means that its value can be changed after object
creation, through methods and operators such as slice assignment.
intbv supports the same operators as
int for arithmetic. In
addition, it provides a number of features to make it suitable for hardware
design. First, the range of allowed values can be constrained. This makes it
possible to check the value at run time during simulation. Moreover, back end
tools can determine the smallest possible bit width for representing the object.
Secondly, it supports bit level operations by providing an indexing and slicing
intbv objects are constructed in general as follows:
intbv([val=None] [, min=None] [, max=None])
val is the initial value. min and max can be used to constrain the value. Following the Python conventions, min is inclusive, and max is exclusive. Therefore, the allowed value range is min .. max-1.
Let’s look at some examples. An unconstrained
intbv object is created
>>> a = intbv(24)
After object creation, min and max are available as attributes for
inspection. Also, the standard Python function
len can be used
to determine the bit width. If we inspect the previously created
object, we get:
>>> a intbv(24) >>> print(a.min) None >>> print(a.max) None >>> len(a) 0
As the instantiation was unconstrained, the min and max attributes
are undefined. Likewise, the bit width is undefined, which is indicated
by a return value
intbv object is created as follows:
>>> a = intbv(24, min=0, max=25)
Inspecting the object now gives:
>>> a intbv(24) >>> a.min 0 >>> a.max 25 >>> len(a) 5
We see that the allowed value range is 0 .. 24, and that 5 bits are required to represent the object.
The min and max bound attributes enable fine-grained control and error checking of the value range. In particular, the bound values do not have to be symmetric or powers of 2. In all cases, the bit width is set appropriately to represent the values in the range. For example:
>>> a = intbv(6, min=0, max=7) >>> len(a) 3 >>> a = intbv(6, min=-3, max=7) >>> len(a) 4 >>> a = intbv(6, min=-13, max=7) >>> len(a) 5
A common requirement in hardware design is access to the individual bits. The
intbv class implements an indexing interface that provides access to
the bits of the underlying two’s complement representation. The following
illustrates bit index read access:
>>> from myhdl import bin >>> a = intbv(24) >>> bin(a) '11000' >>> int(a) 0 >>> int(a) 1 >>> b = intbv(-23) >>> bin(b) '101001' >>> int(b) 1 >>> int(b) 1 >>> int(b) 0
We use the
bin function provide by MyHDL because it shows the two’s
complement representation for negative values, unlike Python’s builtin with the
same name. Note that lower indices correspond to less significant bits. The
following code illustrates bit index assignment:
>>> bin(a) '11000' >>> a = 0 >>> bin(a) '10000' >>> a intbv(16) >>> b intbv(-23) >>> bin(b) '101001' >>> b = 0 >>> bin(b) '100001' >>> b intbv(-31)
intbv type also supports bit slicing, for both read access
assignment. For example:
>>> a = intbv(24) >>> bin(a) '11000' >>> a[4:1] intbv(4) >>> bin(a[4:1]) '100' >>> a[4:1] = 0b001 >>> bin(a) '10010' >>> a intbv(18)
In accordance with the most common hardware convention, and unlike standard Python, slicing ranges are downward. 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 leftmost item.
Both indices can be omitted from the slice. If the rightmost index is omitted,
0 by default. If the leftmost index is omitted, the meaning is to
access “all” higher order bits. For example:
>>> bin(a) '11000' >>> bin(a[4:]) '1000' >>> a[4:] = '0001' >>> bin(a) '10001' >>> a[:] = 0b10101 >>> bin(a) '10101'
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
a[8:] has exactly
8 bits. Likewise, the slice
7-2=5 bits. You can think
about it as follows: for a slice
[i:j], only bits below index
included, and the bit with index
j is the last bit included.
intbv object is sliced, a new
intbv object is returned.
intbv object is always positive, and the value bounds are
set up in accordance with the bit width specified by the slice. For example:
>>> a = intbv(6, min=-3, max=7) >>> len(a) 4 >>> b = a[4:] >>> b intbv(6L) >>> len(b) 4 >>> b.min 0 >>> b.max 16
In the example, the original object is sliced with a slice equal to its bit width. The returned object has the same value and bit width, but its value range consists of all positive values that can be represented by the bit width.
The object returned by a slice is positive, even when the original object is negative:
>>> a = intbv(-3) >>> bin(a, width=5) '11101' >>> b = a[5:] >>> b intbv(29L) >>> bin(b) '11101'
In this example, the bit pattern of the two objects is identical within the bit width, but their values have opposite sign.
Sometimes hardware engineers prefer to constrain an object by defining its bit
width directly, instead of the range of allowed values. Using the slicing
properties of the
intbv class one can do that as follows:
>>> a = intbv(24)[5:]
What actually happens here is that first an unconstrained
is created, which is then sliced. Slicing an
intbv returns a new
intbv with the constraints set up appropriately.
Inspecting the object now shows:
>>> a.min 0 >>> a.max 32 >>> len(a) 5
Note that the max attribute is 32, as with 5 bits it is possible to represent
the range 0 .. 31. Creating an
intbv in this way is convenient but has
the disadvantage that only positive value ranges between 0 and a power of 2 can
In hardware modeling, there is often a need for the elegant modeling of
intbv instances do not support this
automatically, as they assert that any assigned value is within the bound
constraints. However, wrap-around modeling can be straightforward. For
example, the wrap-around condition for a counter is often decoded explicitly,
as it is needed for other purposes. Also, the modulo operator provides an
elegant one-liner in many scenarios:
count.next = (count + 1) % 2**8
However, some interesting cases are not supported by the
For example, we would like to describe a free running counter using a variable
and augmented assignment as follows:
count_var += 1
This is not possible with the
intbv type, as we cannot add the modulo
behavior to this description. A similar problem exist for an augmented left
shift as follows:
shifter <<= 4
To support these operations directly, MyHDL provides the
modbv is implemented as a subclass of
The two classes have an identical interface and work together
in a straightforward way for arithmetic operations.
The only difference is how the bounds are handled: out-of-bound values
result in an error with
intbv, and in wrap-around with
modbv. For example, the modulo counter above can be
modeled as follows:
count = Signal(modbv(0, min=0, max=2**8)) ... count.next = count + 1
The wrap-around behavior is defined in general as follows:
val = (val - min) % (max - min) + min
In a typical case when
min==0, this reduces to:
val = val % max
Unsigned and signed representation¶
intbv is designed to be as high level as possible. The underlying
value of an
intbv object is a Python
int, which is
represented as a two’s complement number with “indefinite” bit
width. The range bounds are only used for error checking, and to
calculate the minimum required bit width for representation. As a
result, arithmetic can be performed like with normal integers.
In contrast, HDLs such as Verilog and VHDL typically require designers
to deal with representational issues, especially for synthesizable code.
They provide low-level types like
arithmetic. The rules for arithmetic with such types are much more
complicated than with plain integers.
In some cases it can be useful to interpret
in terms of “signed” and “unsigned”. Basically, it depends on attribute min.
if min < 0, then the object is “signed”, otherwise it is “unsigned”.
In particular, the bit width of a “signed” object will account for
a sign bit, but that of an “unsigned” will not, because that would
be redundant. From earlier sections, we have learned that the
return value from a slicing operation is always “unsigned”.
In some applications, it is desirable to convert an “unsigned”
intbv to a “signed”, in other words, to interpret the msb bit
as a sign bit. The msb bit is the highest order bit within the object’s
bit width. For this purpose,
intbv provides the
intbv.signed method. For example:
>>> a = intbv(12, min=0, max=16) >>> bin(a) '1100' >>> b = a.signed() >>> b -4 >>> bin(b, width=4) '1100'
intbv.signed extends the msb bit into the higher-order bits of the
underlying object value, and returns the result as an integer.
Naturally, for a “signed” the return value will always be identical
to the original value, as it has the sign bit already.
As an example let’s take a 8 bit wide data bus that would be modeled as follows:
data_bus = intbv(0)[8:]
Now consider that a complex number is transferred over this data bus. The upper 4 bits of the data bus are used for the real value and the lower 4 bits for the imaginary value. As real and imaginary values have a positive and negative value range, we can slice them off from the data bus and convert them as follows:
real.next = data_bus[8:4].signed() imag.next = data_bus[4:].signed()