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% *** CITATION PACKAGES ***
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\usepackage{cite}
\usepackage{xcolor}
\def\comment#1{{\color[rgb]{1.0,0.0,0.0}{#1}}}
+\usepackage{cleveref}
+\crefname{figure}{figure}{figures}
+\newcommand{\fref}[1]{\cref{#1}}
+\newcommand{\Fref}[1]{\Cref{#1}}
+
+
%include polycode.fmt
%include clash.fmt
\subsection{Function application}
The basic syntactic elements of a functional program are functions
- and function application. These have a single obvious \VHDL\
- translation: each top level function becomes a hardware component,
- where each argument is an input port and the result value is the
- (single) output port. This output port can have a complex type (such
- as a tuple), so having just a single output port does not create a
- limitation.
-
- Each function application in turn becomes a component instantiation.
- Here, the result of each argument expression is assigned to a
- signal, which is mapped to the corresponding input port. The output
- port of the function is also mapped to a signal, which is used as
- the result of the application itself.
+ and function application. These have a single obvious translation to a
+ netlist: every function becomes a component, every function argument is an
+ input port and the result value is of a function is an output port. This
+ output port can have a complex type (such as a tuple), so having just a
+ single output port does not create a limitation. Each function application
+ in turn becomes a component instantiation. Here, the result of each
+ argument expression is assigned to a signal, which is mapped to the
+ corresponding input port. The output port of the function is also mapped
+ to a signal, which is used as the result of the application itself.
Since every top level function generates its own component, the
- hierarchy of function calls is reflected in the final \VHDL\
- output as well, creating a hierarchical \VHDL\ description of the
- hardware. This separation in different components makes the
- resulting \VHDL\ output easier to read and debug.
-
- Example that defines the \texttt{mac} function by applying the
- \texttt{add} and \texttt{mul} functions to calculate $a * b + c$:
-
-\begin{code}
-mac a b c = add (mul a b) c
-\end{code}
-
-\begin{figure}
-\centerline{\includegraphics{mac}}
-\caption{Combinatorial Multiply-Accumulate (curried)}
-\label{img:mac-comb}
-\end{figure}
-
-\begin{figure}
-\centerline{\includegraphics{mac-nocurry}}
-\caption{Combinatorial Multiply-Accumulate (uncurried)}
-\label{img:mac-comb-nocurry}
-\end{figure}
+ hierarchy of function calls is reflected in the final netlist aswell,
+ creating a hierarchical description of the hardware. This separation in
+ different components makes the resulting \VHDL\ output easier to read and
+ debug.
+
+ As an example we can see the netlist of the |mac| function in
+ \Cref{img:mac-comb}; the |mac| function applies both the |mul| and |add|
+ function to calculate $a * b + c$:
+ \begin{code}
+ mac a b c = add (mul a b) c
+ \end{code}
+ \begin{figure}
+ \centerline{\includegraphics{mac}}
+ \caption{Combinatorial Multiply-Accumulate}
+ \label{img:mac-comb}
+ \end{figure}
+ The result of using a complex input type can be seen in
+ \cref{img:mac-comb-nocurry} where the |mac| function now uses a single
+ input tuple for the |a|, |b|, and |c| arguments:
+ \begin{code}
+ mac (a, b, c) = add (mul a b) c
+ \end{code}
+ \begin{figure}
+ \centerline{\includegraphics{mac-nocurry}}
+ \caption{Combinatorial Multiply-Accumulate (complex input)}
+ \label{img:mac-comb-nocurry}
+ \end{figure}
\subsection{Choices}
Although describing components and connections allows describing a
sumif _ _ _ = 0
\end{code}
- \comment{TODO: Pretty picture}
+ \begin{figure}
+ \centerline{\includegraphics{choice-ifthenelse}}
+ \caption{Choice - \emph{if-then-else}}
+ \label{img:choice}
+ \end{figure}
+
+ \begin{figure}
+ \centerline{\includegraphics{choice-case}}
+ \caption{Choice - \emph{case-statement / pattern matching}}
+ \label{img:choice}
+ \end{figure}
\subsection{Types}
Translation of two most basic functional concepts has been
In \CLaSH, unconstrained polymorphism is completely supported. Any
function defined can have any number of unconstrained type
- parameters. The \CLaSH compiler will infer the type of every such
+ parameters. The \CLaSH\ compiler will infer the type of every such
argument depending on how the function is applied. There is one
exception to this: The top level function that is translated, can
not have any polymorphic arguments (since it is never applied, so
there is no way to find out the actual types for the type
parameters).
- \CLaSH does not support user-defined type classes, but does use some
+ \CLaSH\ does not support user-defined type classes, but does use some
of the builtin ones for its builtin functions (like \hs{Num} and
\hs{Eq}).
+ \subsection{Higher order}
+ Another powerful abstraction mechanism in functional languages, is
+ the concept of \emph{higher order functions}, or \emph{functions as
+ a first class value}. This allows a function to be treated as a
+ value and be passed around, even as the argument of another
+ function. Let's clarify that with an example:
+
+ \begin{code}
+ notList xs = map not xs
+ \end{code}
+
+ This defines a function \hs{notList}, with a single list of booleans
+ \hs{xs} as an argument, which simply negates all of the booleans in
+ the list. To do this, it uses the function \hs{map}, which takes
+ \emph{another function} as its first argument and applies that other
+ function to each element in the list, returning again a list of the
+ results.
+
+ As you can see, the \hs{map} function is a higher order function,
+ since it takes another function as an argument. Also note that
+ \hs{map} is again a polymorphic function: It does not pose any
+ constraints on the type of elements in the list passed, other than
+ that it must be the same as the type of the argument the passed
+ function accepts. The type of elements in the resulting list is of
+ course equal to the return type of the function passed (which need
+ not be the same as the type of elements in the input list). Both of
+ these can be readily seen from the type of \hs{map}:
+
+ \begin{code}
+ map :: (a -> b) -> [a] -> [b]
+ \end{code}
+
+ As an example from a common hardware design, let's look at the
+ equation of a FIR filter.
+
+ \begin{equation}
+ y_t = \sum\nolimits_{i = 0}^{n - 1} {x_{t - i} \cdot h_i }
+ \end{equation}
+
+ A FIR filter multiplies fixed constants ($h$) with the current and
+ a few previous input samples ($x$). Each of these multiplications
+ are summed, to produce the result at time $t$.
+
+ This is easily and directly implemented using higher order
+ functions. Consider that the vector \hs{hs} contains the FIR
+ coefficients and the vector \hs{xs} contains the current input sample
+ in front and older samples behind. How \hs{xs} gets its value will be
+ show in the next section about state.
+
+ \begin{code}
+ fir ... = foldl1 (+) (zipwith (*) xs hs)
+ \end{code}
+
+ Here, the \hs{zipwith} function is very similar to the \hs{map}
+ function: It takes a function two lists and then applies the
+ function to each of the elements of the two lists pairwise
+ (\emph{e.g.}, \hs{zipwith (+) [1, 2] [3, 4]} becomes
+ \hs{[1 + 3, 2 + 4]}.
+
+ The \hs{foldl1} function takes a function and a single list and applies the
+ function to the first two elements of the list. It then applies to
+ function to the result of the first application and the next element
+ from the list. This continues until the end of the list is reached.
+ The result of the \hs{foldl1} function is the result of the last
+ application.
+
+ As you can see, the \hs{zipwith (*)} function is just pairwise
+ multiplication and the \hs{foldl1 (+)} function is just summation.
+
+ To make the correspondence between the code and the equation even
+ more obvious, we turn the list of input samples in the equation
+ around. So, instead of having the the input sample received at time
+ $t$ in $x_t$, $x_0$ now always stores the current sample, and $x_i$
+ stores the $ith$ previous sample. This changes the equation to the
+ following (Note that this is completely equivalent to the original
+ equation, just with a different definition of $x$ that better suits
+ the \hs{x} from the code):
+
+ \begin{equation}
+ y_t = \sum\nolimits_{i = 0}^{n - 1} {x_i \cdot h_i }
+ \end{equation}
+
+ So far, only functions have been used as higher order values. In
+ Haskell, there are two more ways to obtain a function-typed value:
+ partial application and lambda abstraction. Partial application
+ means that a function that takes multiple arguments can be applied
+ to a single argument, and the result will again be a function (but
+ that takes one argument less). As an example, consider the following
+ expression, that adds one to every element of a vector:
+
+ \begin{code}
+ map ((+) 1) xs
+ \end{code}
+
+ Here, the expression \hs{(+) 1} is the partial application of the
+ plus operator to the value \hs{1}, which is again a function that
+ adds one to its argument.
+
+ A labmda expression allows one to introduce an anonymous function
+ in any expression. Consider the following expression, which again
+ adds one to every element of a list:
+
+ \begin{code}
+ map (\x -> x + 1) xs
+ \end{code}
+
+ Finally, higher order arguments are not limited to just builtin
+ functions, but any function defined in \CLaSH\ can have function
+ arguments. This allows the hardware designer to use a powerful
+ abstraction mechanism in his designs and have an optimal amount of
+ code reuse.
+
+ TODO: Describe ALU example (no code)
+
\subsection{State}
A very important concept in hardware it the concept of state. In a
stateful design, the outputs depend on the history of the inputs, or the
projects / matthijs / master-project / dsd-paper.git / blobdiff