Programming FPGA using Haskell

Here is very short introduction to functional programming language Haskel and functional reasoning. For this we will use CλaSH compiler, that is extension of Haskell language being able to convert large set of Haskell code in either VHLD, Verilog or SystemVerilog for now. Installation instructions are available in hackage doc.

If you need just quick peek in examples, there is source code available of all examples presented here and is compiled in VHLD, Verilog and SystemVerilog. Code is automatically generated, but can be used as quick check of what kind of circuit code describes and what to expect from CλaSH compiler.

Premise is that circuits described functional way are considered simpler to reason about, compose and reuse. We are able to describe topology trough functional composition on abstract way without sacrificing about reasoning, how may registries are in use and where logic gates are. This approach allows describing similar if not same circuits as with Verilog and VHDL using new and fresh approach allowing composing more ambitious synchronous digital circuits.

Descriptions is mathematical in way that is trying to use tools from mathematical theory about composition. It is common to see relations described with mathematical laws.

Function application

Before running coding we should explain how calling functions in Haskell works. In most languages if function takes 2 arguments x and y it is called like f(x,y) where equivalent for this in Haskell is f\ x\ y . This come with advantage because Haskell supports partially applied functions where only one out of two arguments is applied. Lets say that signature of f is Int -> Int -> Bool. We can interpret this as - it takes two integers x an y and it returns boolean. When only first argument x is applied it returns function whit hremaining signature. When applying on function Int -> Int -> Bool first Int remaining is Int -> Bool. That is function that takes Int and it returns Bool.

-- function of 2 Int, that telse if
-- 1st argument is greater then 2nd
greater :: Int -> Int -> Bool
greater x y = x > y

-- takes y and tels whatever it is greater than 2
isgt2 :: Int -> Bool
isgt2 y = greater 2 y

or less verbose as partially applied

isgt2 :: Int -> Bool
isgt2 = greater 2

One way to think about functions in Haskell is that each function takes only one argument and it returns one value that can be function. Signature of greater can be seen as greater :: Int -> ( Int -> Bool ). So it takes an Int and it returns function Int -> Bool. That is function that takes Int and returns Bool.

In Haskell every function returns something. Pure functions, does not execute any action, but just transform input data and return relevant result. Pure functions, has important property that they always returns same output for same input and so they don’t have access to some external time changing data or internal state. In Haskell there is strong force to separate effects from pure functions.

Function composition

Functional programming is about composition. Here it is example of signatures of 2 functions.

funcA2B :: a -> b
funcB2C :: b -> c

funcA2B is function that takes type a and returns b, one example is that it takes Int and returns Bool.funcB2C takes b and it returns c. For example it takes Bool and returns Bool. Having this we are able to create function

funcA2C :: a -> c

by composing together funcA2B and funcB2C to get funcA2C.

For composing two functions we need function

composeFunc :: (b->c) -> (a->b) -> a -> c
composeFunc f g x = f (g x)

First we apply x on g and then we apply result on f.\ Moving back to composeFunc it takes 3 arguments. First two are functions, f with signature b->c and g with signature a->b, 3rd param x is of type a. Function returns type c and is same type that f returns. Looking differently it takes two functions that is b->c and a->b and it returns function a -> c.

For example

minus1 x = x - 1
largeNum x = x > 3

superLargeNum = composeFunc largeNum minus1

superLargeNum is function composed by largeNum and minus1.Type of each function is well defined, but we can either define it manually or compiler can automatically deduce it.

-- simplified types
minus1 :: a -> a
largeNum :: a -> Bool
superLargeNum :: a -> Bool

-- actual types
minus1 :: Num a => a -> a
largeNum :: (Num a, Ord a) => a -> Bool
superLargeNum :: (Num a, Ord a) => a -> Bool

We can read this, that function superLargeNum transforms type a into Bool. Type a has to be defined for class Num and Ord. Num is class that has defined function (-) as minus operator. (among others) and class Ord has defined (>).

composeFunc is commonly used and in Haskell it is defined as operator (.).

(.) = composeFunc

Using this, one can alternatively define superLargeNum as

superLargeNum = largeNum . minus1
-- or
superLargeNum = (> 3) . (- 1)
-- or
superLargeNum x = (x - 1) > 3

Operator . comes from function composition as defined in mathematics f ∘ g . Here we have showed implemention of . as in composeFunc, but sometimes instead of implementation we get only laws. For function (.) holds.

If id is identity function that returns argument

id :: a -> a
id x = x

then function composition laws for ∘ are

id . f == f . id == f
f . g . h == (f . g) . h == f . (g . h)

Instead of having function implementation available we sometimes have available laws that function obeys. It helps reasoning about how function behaves without knowing what it does. Laws also goes hand to hand with implementation.

Functor

Lets take for example type in CλaSH called Signal a. This represent type a changing with clock. Signal is concrete example of functor and a is type embedded in Signal.

Other than function composition of Signal a there is different composition available for functor.

funcA2B :: a -> b
signalA :: Signal a

If we have function of type a -> b and Signal a, than functor enables us to get Signal b

fmap :: (a -> b) -> Signal a -> Signal b

Functor obeys two laws

fmap id == id
fmap (p . q) == (fmap p) . (fmap q)

Or equivalently when applying signal.

fmap id signal == signal
fmap (p . q) signal == fmap p (fmap q signal)

Knowing this we can aswer next question. Does fmap over Signal uses any registries as for example D flip-flop?

Answer is, if that would be case, neither of laws would hold. Form first law it would imply that on left we have signal delayed by 1 clock, and on right non delayed signal and they are not same. Second law would imply delay by 1 clock on left where right side made 2 delays, each by one fmap calls.

To generate core we define function topEntity. Generic type a should be concrete and in this case we will use Signed 7 what is 7 bit signed integer.

topEntity :: Signal (Signed 7) -> Signal Bool
topEntity signal = fmap superLargeNum signal

Executing command fmap can be used as operator. That is <\$>. Alternatively we can implement topEntity also as

topEntity signal = superLargeNum <\$> signal
-- or point free version
topEntity = fmap superLargeNum
-- or using fmap as operator
topEntity = superLargeNum `fmap` signal

It is hard to tell witch version is most common among haskellers, but we can be sure, that due to laws all of them behaves same.

Compiling this exact into Verilog generates code as expected.

Mealy machinery

For generating output based on previous stored value we can use mealy function. Lets implement integrator using mealy machine. With integrator we have in mind function that takes Signal a and produces Signal a and is representing sum of all previous values including current.

integrator signal = mealy mf 0 signal
where
mf state input = (nextstate,output)
where
nextstate = state + input
output = nextstate

mealy is function that takes 3 arguments. First parameter is a function on how to generate next state from state and signal value. Signature of this function is s -> i -> (s,o). In our code that is function called mf. mf is defined in where block for local visibility so can not be used out of\ integrator . What this mf function does it takes 2 arguments state and input and it returns tuple (nextstate,output). Next clock cycle nextstate will be passed again in mf as argument state. 2nd argument of integrator 0 is initial state value. Generated core will provide reset signal that sets state to initial value.

If we now need to create FPGA core, we need to provide topEntity. Here it is full source instantiated with Fixed point arithmetics. We arbitrarily choose SFixed 2 8, This is 10 bit signed fixed point number. It is representing rational numbers from -2 <= x < 2.

module Integrator where
import CLaSH.Prelude

integrator signal = mealy mf 0 signal
where
mf state input = (nextstate,output)
where
nextstate = state + input
output = nextstate

topEntity :: Signal (SFixed 2 8) -> Signal (SFixed 2 8)
topEntity = integrator

integrator can be used in other modules, to build larger and more ambitious circuits. For example to make integrator of integrated signal so that output of first integrator is feed in second one.

intInt = integrator . integrator

Vector

Vec is sized vector with compile type defined size holding elements of same type. It is similar to tuple, except that all elements are of same type.

Vec 5 (Signed 7) is type that holds 5 elements of type Signed 7 what is 7 bit signed number. Vec is also an functor.

For example

vs :: Vec 6 (Signed 9)
vs = 1 :> 2 :> 3 :> 4 :> 5 :> 6 :> Nil

vb :: Vec 6 Bool
vb = fmap (> 3) vs  -- that is <False,False,False,True,True,True>

Operator :> is right-associativity operator that prepends vector on right with value on left. Nil is empty vector and is used, because :> expects vector on right side.

Size of Vec is defined at compile time and can be generic. Lets take look on one of many functions from Vec libraray that is replicate and has prototype

replicate :: SNat n -> a -> Vec n a
-- "replicate n a" returns a vector that has `n` copies of `a`.

Vec 5 a is not same as Vec 6 a and it means that replicate returns different type based on argument n. For this to work, n is of type SNat x and is different type for each x. Value that has type Signed 5 as 5 bit signed value can be -2 or 7, where value d3 that has type SNat 3 represents number 3 as unique type.

Moving average

Moving average is simple low pass FIR filter expressed as sum of last n values. We will deliberately skip division in our example, so we will in reality talk about moving sum.

Here is straight forward and naive option to describe moving average using mealy.

movingAvarage n signal = mealy mf (replicate n 0) signal
where
mf state input = (nexts,output)
where
nexts = input :> init state
output = fold (+) nexts

First argument n is number of accumulated value is also size of Vec used as internal state storage. Type of internal state is deduced as Vec n a and is deduced from type of initial state value replicate n 0 that returns Vec that returns n copies of 0.

init :: Vec (n + 1) a -> Vec n a is function that takes Vec with size n+1 and returns all except last element that is Vec of size n.

nexts expressed as next state is vector right shifted by previous value and perpended :> with current clock input value. Operation works as fifo shift register.

fold is function best expressed with same picture as in manuals

From signature we can conclude that fold :: (a -> a -> a) -> Vec (n + 1) a -> a takes 2 arguments. First is function of type (a -> a -> a) an second argument is of type Vec (n + 1) a. Function is than applied over vector in tree like structure, enabling minimum latency. Function f from out case is function (+) (in haskell operator in brakets is used as function). and has prototype a -> a -> a and is applied over state.

There is better

Lets try some optimization of moving average from previous section. Observing totals we can conclude we don’t need that many adders. Total output is changed at each clock only by value that enters fifo from one side and value that pops out of fifo on other. At each clock we need to subtract value that pops out and add value that enters from total. This comes at expanse of 2 extra sized registers (previous solution can be reduced by 1 sized register) but requires only 2 sized adders.

movingAvarage2 n signal = mealy mf ((replicate n 0),0) signal
where
mf (vec,total) input = ((nextVec,nextTot),output)
where
output = total + input - (last vec)
nextVec = input :> init vec
nextTot = output

mf has at this point signature (Vec n a,a) -> a -> ((Vec n a,a),a). Internal state type is tuple where first value is vec used as fifo and total accumulated in fifp.vecandtotalare unpacked from tuple in input argument, so we are able to access this value, without unpacking them in body. Equivalent, but more verbosemf` implemetaion is sketched as

mf state input = ...
where
vec = fst state -- first element of tuple
total = snd state -- second element of tuple
output = ...

Circuit composition

Great power of composition comes from separating logic into reusable parts and then composing them together in larger blocks. Function (.) and fold are good examples we have used so far. Now lets try something similar, by designing feedback loop.

+---+    +---+
input -->-| f |-->-| g |-->-+-->-- output
+---+    +---+    |
|               ∨
∧      +---+    |
+----<-| h |-<--+
+---+

First lets figure out prototypes of each function f, g and h.

f :: Signal a -> Signal b -> Signal c
g :: Signal c -> Signal d
h :: Signal d -> Signal b

and feedback loop can be written directly from circuit diagram as

feedbackLoop :: (Signal a -> Signal b -> Signal c)
-> (Signal c -> Signal d)
-> (Signal d -> Signal b)
-> Signal a
-> Signal d
feedbackLoop f g h input = out where
out = g fout
fout = f input (h out)

If user has available functions that works over primitive values like Signed instead Signal (Signed 16) for example, he can use fold. For example if

g :: Signed 16 -> Signed 16

Then gs working over signal would be constructed from g

gs :: Signal (Signed 16) -> Signal (Signed 16)
gs = fmap g
-- or more verbose
gs sig = fmap g sig

For function over 2 input arguments we can use library function liftA2

liftA2 :: (a -> b -> c) -> functor a -> functor b -> functor c

Where functor is in our example Signal For example to get function minus (-) to operate over Signal we need to apply liftA2.

signalMinus :: Signal a -> Signal a -> Signal a
signalMinus = liftA2 (-)

To construct negative feedback looking like

+---+
input -->-( - )-->-| g |-->-+-->-- output
|      +---+    |
∧               ∨
+--------<------+

Using feedbackLoop,

negativeFeedback :: Num a =>
(Signal a -> Signal a) ->
Signal a ->
Signal a
negativeFeedback g = feedbackLoop (liftA2 (-)) g id

We have replaced h by id and f argument is liftA2 (-)

There is subtle issue with this and in some cases this circuit can not be synthesized. That is because we have feedback loop, so we have made sure that there is delay in g function. If g function is for example id or fmap id so it works like wire this circuit is not stable any more. To overcome this, we have to introduce delay in feedback. Function h from previous diagram, becomes register 0 instead of id. function

negativeFeedback :: Num a => (Signal a -> Signal a) -> Signal a -> Signal a
negativeFeedback g = feedbackLoop (liftA2 (-)) g (register 0)

register :: a -> Signal a -> Signal a is a function, that takes inital value, that outputs this value before remaining of stream. register is available in CλaSH libraray, but can be implemented using mealy as

register initialValue signal = mealy mf initialValue
where
mf prevS input = (input,prevS)

What is even more surprising is that it can be done even vice versa. That is mealey can be expressed in terms of register. For details check examples on github

Conclusion

There are lot of features that were no covered. But are invaluable during development

• repl, (read eval print loop) that allows interactive development, type deduction
• ability to simulate in repl or compile in CPU native executable
• testebenches that generates VHDL / Verilog / Systemverilog and can be used directly in IDE
• full featured library, (ram and rom access and initalisation, multiple clock domains, type safe delays … )

From my and experience of many it takes lot of time and practice to grasp new programming language, what Haskell for those not familiar with functional programming certainly is. Reader with interest will be able to find lot of free resources available online for all levels of experience with open and active community.