Language guide
Warning
This guide is a work in progress and is seriously incomplete!
This guide introduces the nMigen language in depth. It assumes familiarity with synchronous digital logic and the Python programming language, but does not require prior experience with any hardware description language. See the tutorial for a stepbystep introduction to the language.
The prelude
nMigen is a regular Python library, so needs to be imported before
use. The root nmigen
module, called the prelude, is carefully
curated to export a small amount of the most essential names, useful in
nearly every design. In source files dedicated to nMigen code, it is
bad practice to use a glob import
but for convenience for the purposes of this tutorial it is done here:
from nmigen import * # strongly discouraged but convenient
However as is described in many tutorials and programming “best practice” documents across the internet, wildcard (glob) imports quickly creates unmanageable and unmaintainable projects, especially when wildcardimporting from several different libraries. If a source file uses nMigen together with many other libraries, it is conventional to use a short alias instead:
import nmigen as nm # useful in very large projects
Best practice is to import the individual features needed: standard Python linter tools may then be used. It is also clearer as to what any given nMigen module actually uses.
Values
The basic building block of the nMigen language is a value, which is a term for a binary number that is computed or stored anywhere in the design. Each value has a width—the amount of bits used to represent the value—and a signedness—the interpretation of the value by arithmetic operations—collectively called its shape. Signed values always use two’s complement representation.
Constants
The simplest nMigen value is a constant, representing a fixed number, and introduced using Const(...)
or its short alias C(...)
:
>>> ten = Const(10)
>>> minus_two = C(2)
The code above does not specify any shape for the constants. If the shape is omitted, nMigen uses unsigned shape for positive numbers and signed shape for negative numbers, with the width inferred from the smallest amount of bits necessary to represent the number. As a special case, in order to get the same inferred shape for True
and False
, 0
is considered to be 1bit unsigned.
>>> ten.shape()
unsigned(4)
>>> minus_two.shape()
signed(2)
>>> C(0).shape()
unsigned(1)
The shape of the constant can be specified explicitly, in which case the number’s binary representation will be truncated or extended to fit the shape. Although rarely useful, 0bit constants are permitted.
>>> Const(360, unsigned(8)).value
104
>>> Const(129, signed(8)).value
127
>>> Const(1, unsigned(0)).value
0
Shapes
A Shape
is an object with two attributes, .width
and .signed
. It can be constructed directly:
>>> Shape(width=5, signed=False)
unsigned(5)
>>> Shape(width=12, signed=True)
signed(12)
However, in most cases, the shape is always constructed with the same signedness, and the aliases signed
and unsigned
are more convenient:
>>> unsigned(5) == Shape(width=5, signed=False)
True
>>> signed(12) == Shape(width=12, signed=True)
True
Shapes of values
All values have a .shape()
method that computes their shape. The width of a value v
, v.shape().width
, can also be retrieved with len(v)
.
>>> Const(5).shape()
unsigned(3)
>>> len(Const(5))
3
Shape casting
Shapes can be cast from other objects, which are called shapecastable. Casting is a convenient way to specify a shape indirectly, for example, by a range of numbers representable by values with that shape.
Casting to a shape can be done explicitly with Shape.cast
, but is usually implicit, since shapecastable objects are accepted anywhere shapes are.
Shapes from integers
Casting a shape from an integer i
is a shorthand for constructing a shape with unsigned(i)
:
>>> Shape.cast(5)
unsigned(5)
>>> C(0, 3).shape()
unsigned(3)
Shapes from ranges
Casting a shape from a range
r
produces a shape that:
has a width large enough to represent both
min(r)
andmax(r)
, andis signed if either
min(r)
ormax(r)
are negative, unsigned otherwise.
Specifying a shape with a range is convenient for counters, indexes, and all other values whose width is derived from a set of numbers they must be able to fit:
>>> Const(0, range(100)).shape()
unsigned(7)
>>> items = [1, 2, 3]
>>> C(1, range(len(items))).shape()
unsigned(2)
Warning
Python ranges are exclusive or halfopen, meaning they do not contain their .stop
element. Because of this, values with shapes cast from a range(stop)
where stop
is a power of 2 are not wide enough to represent stop
itself:
>>> fencepost = C(256, range(256))
>>> fencepost.shape()
unsigned(8)
>>> fencepost.value
0
Be mindful of this edge case!
Shapes from enumerations
Casting a shape from an enum.Enum
subclass E
:
fails if any of the enumeration members have noninteger values,
has a width large enough to represent both
min(m.value for m in E)
andmax(m.value for m in E)
, andis signed if either
min(m.value for m in E)
ormax(m.value for m in E)
are negative, unsigned otherwise.
Specifying a shape with an enumeration is convenient for finite state machines, multiplexers, complex control signals, and all other values whose width is derived from a few distinct choices they must be able to fit:
class Direction(enum.Enum):
TOP = 0
LEFT = 1
BOTTOM = 2
RIGHT = 3
>>> Shape.cast(Direction)
unsigned(2)
Note
The enumeration does not have to subclass enum.IntEnum
; it only needs to have integers as values of every member. Using enumerations based on enum.Enum
rather than enum.IntEnum
prevents unwanted implicit conversion of enum members to integers.
Value casting
Like shapes, values may be cast from other objects, which are called valuecastable. Casting allows objects that are not provided by nMigen, such as integers or enumeration members, to be used in nMigen expressions directly.
Casting to a value can be done explicitly with Value.cast
, but is usually implicit, since valuecastable objects are accepted anywhere values are.
Values from integers
Casting a value from an integer i
is a shorthand for Const(i)
:
>>> Value.cast(5)
(const 3'd5)
Values from enumeration members
Casting a value from an enumeration member m
is a shorthand for Const(m.value, type(m))
:
>>> Value.cast(Direction.LEFT)
(const 2'd1)
Signals
A signal is a value representing a (potentially) varying number. Signals can be assigned in a combinatorial or synchronous domain, in which case they are generated as wires or registers, respectively. Signals always have a welldefined value; they cannot be uninitialized or undefined.
Signal shapes
A signal can be created with an explicitly specified shape (any shapecastable object); if omitted, the shape defaults to unsigned(1)
. Although rarely useful, 0bit signals are permitted.
>>> Signal().shape()
unsigned(1)
>>> Signal(4).shape()
unsigned(4)
>>> Signal(range(8, 7)).shape()
signed(4)
>>> Signal(Direction).shape()
unsigned(2)
>>> Signal(0).shape()
unsigned(0)
Signal names
Each signal has a name, which is used in the waveform viewer, diagnostic messages, Verilog output, and so on. In most cases, the name is omitted and inferred from the name of the variable or attribute the signal is placed into:
>>> foo = Signal()
>>> foo.name
'foo'
>>> self.bar = Signal()
>>> self.bar.name
'bar'
However, the name can also be specified explicitly with the name=
parameter:
>>> foo2 = Signal(name="second_foo")
>>> foo2.name
'second_foo'
The names do not need to be unique; if two signals with the same name end up in the same namespace while preparing for simulation or synthesis, one of them will be renamed to remove the ambiguity.
Initial signal values
Each signal has an initial value, specified with the reset=
parameter. If the initial value is not specified explicitly, zero is used by default. An initial value can be specified with an integer or an enumeration member.
Signals assigned in a combinatorial domain assume their initial value when none of the assignments are active. Signals assigned in a synchronous domain assume their initial value after poweron reset and, unless the signal is resetless, explicit reset. Signals that are used but never assigned are equivalent to constants of their initial value.
>>> Signal(4).reset
0
>>> Signal(4, reset=5).reset
5
>>> Signal(Direction, reset=Direction.LEFT).reset
1
Resetless signals
Signals assigned in a synchronous domain can be resettable or resetless, specified with the reset_less=
parameter. If the parameter is not specified, signals are resettable by default. Resettable signals assume their initial value on explicit reset, which can be asserted via the clock domain or by using ResetInserter
. Resetless signals are not affected by explicit reset.
Signals assigned in a combinatorial domain are not affected by the reset_less
parameter.
>>> Signal().reset_less
False
>>> Signal(reset_less=True).reset_less
True
Operators
To describe computations, nMigen values can be combined with each other or with valuecastable objects using a rich array of arithmetic, bitwise, logical, bit sequence, and other operators to form expressions, which are themselves values.
Performing or describing computations?
Code written in the Python language performs computations on concrete objects, like integers, with the goal of calculating a concrete result:
>>> a = 5
>>> a + 1
6
In contrast, code written in the nMigen language describes computations on abstract objects, like signals, with the goal of generating a hardware circuit that can be simulated, synthesized, and so on. nMigen expressions are ordinary Python objects that represent parts of this circuit:
>>> a = Signal(8, reset=5)
>>> a + 1
(+ (sig a) (const 1'd1))
Although the syntax is similar, it is important to remember that nMigen values exist on a higher level of abstraction than Python values. For example, expressions that include nMigen values cannot be used in Python control flow structures:
>>> if a == 0:
... print("Zero!")
Traceback (most recent call last):
...
TypeError: Attempted to convert nMigen value to Python boolean
Because the value of a
, and therefore a == 0
, is not known at the time when the if
statement is executed, there is no way to decide whether the body of the statement should be executed—in fact, if the design is synthesized, by the time a
has any concrete value, the Python program has long finished! To solve this problem, nMigen provides its own control structures that, also, manipulate circuits.
Width extension
Many of the operations described below (for example, addition, equality, bitwise OR, and part select) extend the width of one or both operands to match the width of the expression. When this happens, unsigned values are always zeroextended and signed values are always signextended regardless of the operation or signedness of the result.
Arithmetic operators
Most arithmetic operations on integers provided by Python can be used on nMigen values, too.
Although Python integers have unlimited precision and nMigen values are represented with a finite amount of bits, arithmetics on nMigen values never overflows because the width of the arithmetic expression is always sufficient to represent all possible results.
>>> a = Signal(8)
>>> (a + 1).shape() # needs to represent 1 to 256
unsigned(9)
Similarly, although Python integers are always signed and nMigen values can be either signed or unsigned, if any of the operands of an nMigen arithmetic expression is signed, the expression itself is also signed, matching the behavior of Python.
>>> a = Signal(unsigned(8))
>>> b = Signal(signed(8))
>>> (a + b).shape() # needs to represent 128 to 382
signed(10)
While arithmetic computations never result in an overflow, assigning their results to signals may truncate the most significant bits.
The following table lists the arithmetic operations provided by nMigen:
Operation 
Description 


addition 

negation 

subtraction 

multiplication 

floor division 

modulo 

absolute value 
Comparison operators
All comparison operations on integers provided by Python can be used on nMigen values. However, due to a limitation of Python, chained comparisons (e.g. a < b < c
) cannot be used.
Similar to arithmetic operations, if any operand of a comparison expression is signed, a signed comparison is performed. The result of a comparison is a 1bit unsigned value.
The following table lists the comparison operations provided by nMigen:
Operation 
Description 


equality 

inequality 

less than 

less than or equal 

greater than 

greater than or equal 
Bitwise, shift, and rotate operators
All bitwise and shift operations on integers provided by Python can be used on nMigen values as well.
Similar to arithmetic operations, if any operand of a bitwise expression is signed, the expression itself is signed as well. A shift expression is signed if the shifted value is signed. A rotate expression is always unsigned.
Rotate operations with variable rotate amounts cannot be efficiently synthesized for nonpowerof2 widths of the rotated value. Because of that, the rotate operations are only provided for constant rotate amounts, specified as Python int
s.
The following table lists the bitwise and shift operations provided by nMigen:
Operation 
Description 
Notes 


bitwise NOT; complement 


bitwise AND 


bitwise OR 


bitwise XOR 


bitwise IMPLY 


arithmetic right shift by variable amount 


left shift by variable amount 


left rotate by constant amount 


right rotate by constant amount 


left shift by constant amount 


right shift by constant amount 
Note
Because nMigen ensures that the width of a variable left shift expression is wide enough to represent any possible result, variable left shift by a wide amount produces exponentially wider intermediate values, stressing the synthesis tools:
>>> (1 << C(0, 32)).shape()
unsigned(4294967296)
Although nMigen will detect and reject expressions wide enough to break other tools, it is a good practice to explicitly limit the width of a shift amount in a variable left shift.
Reduction operators
Bitwise reduction operations on integers are not provided by Python, but are very useful for hardware. They are similar to bitwise operations applied “sideways”; for example, if bitwise AND is a binary operator that applies AND to each pair of bits between its two operands, then reduction AND is an unary operator that applies AND to all of the bits in its sole operand.
The result of a reduction is a 1bit unsigned value.
The following table lists the reduction operations provided by nMigen:
Operation 
Description 
Notes 


reduction AND; are all bits set? 


reduction OR; is any bit set? 


reduction XOR; is an odd number of bits set? 


conversion to boolean; is nonzero? 
Conceptually the same as applying the Python all()
or any()
function to the value viewed as a collection of bits.
Conceptually the same as applying the Python bool()
function to the value viewed as an integer.
Logical operators
Unlike the arithmetic or bitwise operators, it is not possible to change the behavior of the Python logical operators not
, and
, and or
. Due to that, logical expressions in nMigen are written using bitwise operations on boolean (1bit unsigned) values, with explicit boolean conversions added where necessary.
The following table lists the Python logical expressions and their nMigen equivalents:
Python expression 
nMigen expression (any operands) 







When the operands are known to be boolean values, such as comparisons, reductions, or boolean signals, the .bool()
conversion may be omitted for clarity:
Python expression 
nMigen expression (boolean operands) 







Warning
Because of Python operator precedence, logical operators bind less tightly than comparison operators whereas bitwise operators bind more tightly than comparison operators. As a result, all logical expressions in nMigen must have parenthesized operands.
Omitting parentheses around operands in an nMigen a logical expression is likely to introduce a subtle bug:
>>> en = Signal()
>>> addr = Signal(8)
>>> en & (addr == 0) # correct
(& (sig en) (== (sig addr) (const 1'd0)))
>>> en & addr == 0 # WRONG! addr is truncated to 1 bit
(== (& (sig en) (sig addr)) (const 1'd0))
Warning
When applied to nMigen boolean values, the ~
operator computes negation, and when applied to Python boolean values, the not
operator also computes negation. However, the ~
operator applied to Python boolean values produces an unexpected result:
>>> ~False
1
>>> ~True
2
Because of this, Python booleans used in nMigen logical expressions must be negated with the not
operator, not the ~
operator. Negating a Python boolean with the ~
operator in an nMigen logical expression is likely to introduce a subtle bug:
>>> stb = Signal()
>>> use_stb = True
>>> (not use_stb)  stb # correct
( (const 1'd0) (sig stb))
>>> ~use_stb  stb # WRONG! MSB of 2bit wide OR expression is always 1
( (const 2'sd2) (sig stb))
nMigen automatically detects some cases of misuse of ~
and emits a detailed diagnostic message.
Bit sequence operators
Apart from acting as numbers, nMigen values can also be treated as bit sequences, supporting slicing, concatenation, replication, and other sequence operations. Since some of the operators Python defines for sequences clash with the operators it defines for numbers, nMigen gives these operators a different name. Except for the names, nMigen values follow Python sequence semantics, with the least significant bit at index 0.
Because every nMigen value has a single fixed width, bit slicing and replication operations require the subscripts and count to be constant, specified as Python int
s. It is often useful to slice a value with a constant width and variable offset, but this cannot be expressed with the Python slice notation. To solve this problem, nMigen provides additional part select operations with the necessary semantics.
The result of any bit sequence operation is an unsigned value.
The following table lists the bit sequence operations provided by nMigen:
Operation 
Description 
Notes 


bit length; value width 


bit slicing by constant subscripts 


bit iteration 


overlapping part select with variable offset 


nonoverlapping part select with variable offset 


concatenation 


replication 
Words “length” and “width” have the same meaning when talking about nMigen values. Conventionally, “width” is used.
All variations of the Python slice notation are supported, including “extended slicing”. E.g. all of a[0]
, a[1:9]
, a[2:]
, a[:2]
, a[::1]
, a[0:8:2]
select bits in the same way as other Python sequence types select their elements.
In the concatenated value, a
occupies the least significant bits, and b
the most significant bits.
For the operators introduced by nMigen, the following table explains them in terms of Python code operating on tuples of bits rather than nMigen values:
nMigen operation 
Equivalent Python code 









Warning
In Python, the digits of a number are written righttoleft (0th exponent at the right), and the elements of a sequence are written lefttoright (0th element at the left). This mismatch can cause confusion when numeric operations (like shifts) are mixed with bit sequence operations (like concatenations). For example, Cat(C(0b1001), C(0b1010))
has the same value as C(0b1010_1001)
, val[4:]
is equivalent to val >> 4
, and val[1]
refers to the most significant bit.
Such confusion can often be avoided by not using numeric and bit sequence operations in the same expression. For example, although it may seem natural to describe a shift register with a numeric shift and a sequence slice operations, using sequence operations alone would make it easier to understand.
Note
Could nMigen have used a different indexing or iteration order for values? Yes, but it would be necessary to either place the most significant bit at index 0, or deliberately break the Python sequence type interface. Both of these options would cause more issues than using different iteration orders for numeric and sequence operations.
Conversion operators
The .as_signed()
and .as_unsigned()
conversion operators reinterpret the bits of a value with the requested signedness. This is useful when the same value is sometimes treated as signed and sometimes as unsigned, or when a signed value is constructed using slices or concatenations. For example, (pc + imm[:7].as_signed()).as_unsigned()
signextends the 7 least significant bits of imm
to the width of pc
, performs the addition, and produces an unsigned result.
Choice operator
The Mux(sel, val1, val0)
choice expression (similar to the conditional expression in Python) is equal to the operand val1
if sel
is nonzero, and to the other operand val0
otherwise. If any of val1
or val0
are signed, the expression itself is signed as well.
Modules
A module is a unit of the nMigen design hierarchy: the smallest collection of logic that can be independently simulated, synthesized, or otherwise processed. Modules associate signals with control domains, provide control structures, manage clock domains, and aggregate submodules.
Every nMigen design starts with a fresh module:
>>> m = Module()
Control domains
A control domain is a named group of signals that change their value in identical conditions.
All designs have a single predefined combinatorial domain, containing all signals that change immediately when any value used to compute them changes. The name comb
is reserved for the combinatorial domain.
A design can also have any amount of userdefined synchronous domains, also called clock domains, containing signals that change when a specific edge occurs on the domain’s clock signal or, for domains with asynchronous reset, on the domain’s reset signal. Most modules only use a single synchronous domain, conventionally called sync
, but the name sync
does not have to be used, and lacks any special meaning beyond being the default.
The behavior of assignments differs for signals in combinatorial and synchronous domains. Collectively, signals in synchronous domains contain the state of a design, whereas signals in the combinatorial domain cannot form feedback loops or hold state.
Assigning to signals
Assignments are used to change the values of signals. An assignment statement can be introduced with the .eq(...)
syntax:
>>> s = Signal()
>>> s.eq(1)
(eq (sig s) (const 1'd1))
Similar to how nMigen operators work, an nMigen assignment is an ordinary Python object used to describe a part of a circuit. An assignment does not have any effect on the signal it changes until it is added to a control domain in a module. Once added, it introduces logic into the circuit generated from that module.
Assignment targets
The target of an assignment can be more complex than a single signal. It is possible to assign to any combination of signals, bit slices, concatenations, and part selects as long as it includes no other values:
>>> a = Signal(8)
>>> b = Signal(4)
>>> Cat(a, b).eq(0)
(eq (cat (sig a) (sig b)) (const 1'd0))
>>> a[:4].eq(b)
(eq (slice (sig a) 0:4) (sig b))
>>> Cat(a, a).bit_select(b, 2).eq(0b11)
(eq (part (cat (sig a) (sig a)) (sig b) 2 1) (const 2'd3))
Assignment domains
The m.d.<domain> += ...
syntax is used to add assignments to a specific control domain in a module. It can add just a single assignment, or an entire sequence of them:
a = Signal()
b = Signal()
c = Signal()
m.d.comb += a.eq(1)
m.d.sync += [
b.eq(c),
c.eq(b),
]
If the name of a domain is not known upfront, the m.d["<domain>"] += ...
syntax can be used instead:
def add_toggle(num):
t = Signal()
m.d[f"sync_{num}"] += t.eq(~t)
add_toggle(2)
Every signal included in the target of an assignment becomes a part of the domain, or equivalently, driven by that domain. A signal can be either undriven or driven by exactly one domain; it is an error to add two assignments to the same signal to two different domains:
>>> d = Signal()
>>> m.d.comb += d.eq(1)
>>> m.d.sync += d.eq(0)
Traceback (most recent call last):
...
nmigen.hdl.dsl.SyntaxError: Driverdriver conflict: trying to drive (sig d) from d.sync, but it is already driven from d.comb
Note
Clearly, nMigen code that drives a single bit of a signal from two different domains does not describe a meaningful circuit. However, driving two different bits of a signal from two different domains does not inherently cause such a conflict. Would nMigen accept the following code?
e = Signal(2)
m.d.comb += e[0].eq(0)
m.d.sync += e[1].eq(1)
The answer is no. While this kind of code is occasionally useful, rejecting it greatly simplifies backends, simulators, and analyzers.
Assignment order
Unlike with two different domains, adding multiple assignments to the same signal to the same domain is welldefined.
Assignments to different signal bits apply independently. For example, the following two snippets are equivalent:
a = Signal(8)
m.d.comb += [
a[0:4].eq(C(1, 4)),
a[4:8].eq(C(2, 4)),
]
a = Signal(8)
m.d.comb += a.eq(Cat(C(1, 4), C(2, 4)))
If multiple assignments change the value of the same signal bits, the assignment that is added last determines the final value. For example, the following two snippets are equivalent:
b = Signal(9)
m.d.comb += [
b[0:9].eq(Cat(C(1, 3), C(2, 3), C(3, 3))),
b[0:6].eq(Cat(C(4, 3), C(5, 3))),
b[3:6].eq(C(6, 3)),
]
b = Signal(9)
m.d.comb += b.eq(Cat(C(4, 3), C(6, 3), C(3, 3)))
Multiple assignments to the same signal bits are more useful when combined with control structures, which can make some of the assignments active or inactive. If all assignments to some signal bits are inactive, their final values are determined by the signal’s domain, combinatorial or synchronous.
Control structures
Although it is possible to write any decision tree as a combination of assignments and choice expressions, nMigen provides control structures tailored for this task: If, Switch, and FSM. The syntax of all control structures is based on context managers and uses with
blocks, for example:
timer = Signal(8)
with m.If(timer == 0):
m.d.sync += timer.eq(10)
with m.Else():
m.d.sync += timer.eq(timer  1)
While some nMigen control structures are superficially similar to imperative control flow statements (such as Python’s if
), their function—together with expressions and assignments—is to describe circuits. The code above is equivalent to:
timer = Signal(8)
m.d.sync += timer.eq(Mux(timer == 0, 10, timer  1))
Because all branches of a decision tree affect the generated circuit, all of the Python code inside nMigen control structures is always evaluated in the order in which it appears in the program. This can be observed through Python code with side effects, such as print()
:
timer = Signal(8)
with m.If(timer == 0):
print("inside `If`")
m.d.sync += timer.eq(10)
with m.Else():
print("inside `Else`")
m.d.sync += timer.eq(timer  1)
inside `If`
inside `Else`
Active and inactive assignments
An assignment added inside an nMigen control structure, i.e. with m.<...>:
block, is active if the condition of the control structure is satisfied, and inactive otherwise. For any given set of conditions, the final value of every signal assigned in a module is the same as if the inactive assignments were removed and the active assignments were performed unconditionally, taking into account the assignment order.
For example, there are two possible cases in the circuit generated from the following code:
timer = Signal(8)
m.d.sync += timer.eq(timer  1)
with m.If(timer == 0):
m.d.sync += timer.eq(10)
When timer == 0
is true, the code reduces to:
m.d.sync += timer.eq(timer  1)
m.d.sync += timer.eq(10)
Due to the assignment order, it further reduces to:
m.d.sync += timer.eq(10)
When timer == 0
is false, the code reduces to:
m.d.sync += timer.eq(timer  1)
Combining these cases together, the code above is equivalent to:
timer = Signal(8)
m.d.sync += timer.eq(Mux(timer == 0, 10, timer  1))
Combinatorial evaluation
Signals in the combinatorial control domain change whenever any value used to compute them changes. The final value of a combinatorial signal is equal to its initial value updated by the active assignments in the assignment order. Combinatorial signals cannot hold any state.
Consider the following code:
a = Signal(8, reset=1)
with m.If(en):
m.d.comb += a.eq(b + 1)
Whenever the signals en
or b
change, the signal a
changes as well. If en
is false, the final value of a
is its initial value, 1
. If en
is true, the final value of a
is equal to b + 1
.
A combinatorial signal that is computed directly or indirectly based on its own value is a part of a combinatorial feedback loop, sometimes shortened to just feedback loop. Combinatorial feedback loops can be stable (i.e. implement a constant driver or a transparent latch), or unstable (i.e. implement a ring oscillator). nMigen prohibits using assignments to describe any kind of a combinatorial feedback loop, including transparent latches.
Warning
The current version of nMigen does not detect combinatorial feedback loops, but processes the design under the assumption that there aren’t any. If the design does in fact contain a combinatorial feedback loop, it will likely be silently miscompiled, though some cases will be detected during synthesis or place & route.
This hazard will be eliminated in the future.
Note
In the exceedingly rare case when a combinatorial feedback loop is desirable, it is possible to implement it by directly instantiating technology primitives (e.g. devicespecific LUTs or latches). This is also the only way to introduce a combinatorial feedback loop with welldefined behavior in simulation and synthesis, regardless of the HDL being used.
Synchronous evaluation
Signals in synchronous control domains change whenever a specific transition (positive or negative edge) occurs on the clock of the synchronous domain. In addition, the signals in clock domains with an asynchronous reset change when such a reset is asserted. The final value of a synchronous signal is equal to its initial value if the reset (of any type) is asserted, or to its current value updated by the active assignments in the assignment order otherwise. Synchronous signals always hold state.