%
\documentclass[conference,pdf,a4paper,10pt,final,twoside,twocolumn]{IEEEtran}
+\IEEEoverridecommandlockouts
% Add the compsoc option for Computer Society conferences.
%
% If IEEEtran.cls has not been installed into the LaTeX system files,
\IEEEauthorblockA{%Computer Architecture for Embedded Systems (CAES)\\
Department of EEMCS, University of Twente\\
P.O. Box 217, 7500 AE, Enschede, The Netherlands\\
-c.p.r.baaij@@utwente.nl, matthijs@@stdin.nl, j.kuper@@utwente.nl}}
+c.p.r.baaij@@utwente.nl, matthijs@@stdin.nl, j.kuper@@utwente.nl}
+% \thanks{Supported through FP7 project: S(o)OS (248465)}
+}
% \and
% \IEEEauthorblockN{Homer Simpson}
% \IEEEauthorblockA{Twentieth Century Fox\\
ForSyDe1,Wired,reFLect}. The idea of using functional languages for hardware
descriptions started in the early 1980s \cite{Cardelli1981, muFP,DAISY,FHDL},
a time which also saw the birth of the currently popular hardware description
-languages such as \VHDL. The merit of using a functional language to describe
-hardware comes from the fact that combinatorial circuits can be directly
-modeled as mathematical functions and that functional languages are very good
-at describing and composing mathematical functions.
-
-In an attempt to decrease the amount of work involved with creating all the
-required tooling, such as parsers and type-checkers, many functional hardware
-description languages are embedded as a domain specific language inside the
-functional language Haskell \cite{Hydra,Hawk1,Lava,ForSyDe1,Wired}. This
-means that a developer is given a library of Haskell~\cite{Haskell} functions
-and types that together form the language primitives of the domain specific
-language. As a result of how the signals are modeled and abstracted, the
-functions used to describe a circuit also build a large domain-specific
-datatype (hidden from the designer) which can then be processed further by an
-embedded compiler. This compiler actually runs in the same environment as the
-description; as a result compile-time and run-time become hard to define, as
-the embedded compiler is usually compiled by the same Haskell compiler as the
-circuit description itself.
+languages such as \VHDL. Functional languages are especially suited to
+describe hardware because combinational circuits can be directly modeled
+as mathematical functions and that functional languages are very good at
+describing and composing mathematical functions.
+
+In an attempt to decrease the amount of work involved in creating all the
+required tooling, such as parsers and type-checkers, many functional
+hardware description languages \cite{Hydra,Hawk1,Lava,ForSyDe1,Wired}
+are embedded as a domain specific language inside the functional
+language Haskell \cite{Haskell}. This means that a developer is given a
+library of Haskell functions and types that together form the language
+primitives of the domain specific language. The primitive functions used
+to describe a circuit do not actually process any signals, but instead
+compose a large domain-specific datatype (which is usually hidden from
+the designer). This datatype is then further processed by an embedded
+circuit compiler. This circuit compiler actually runs in the same
+environment as the description; as a result compile-time and run-time
+become hard to define, as the embedded circuit compiler is usually
+compiled by the same Haskell compiler as the circuit description itself.
The approach taken in this research is not to make another domain specific
language embedded in Haskell, but to use (a subset of) the Haskell language
itself for the purpose of describing hardware. By taking this approach, we can
capture certain language constructs, such as Haskell's choice elements
-(if-constructs, case-constructs, pattern matching, etc.), which are not
+(if-expressions, case-expressions, pattern matching, etc.), which are not
available in the functional hardware description languages that are embedded
in Haskell as a domain specific language. As far as the authors know, such
extensive support for choice-elements is new in the domain of functional
hardware description languages. As the hardware descriptions are plain Haskell
-functions, these descriptions can be compiled for simulation using an
-optimizing Haskell compiler such as the Glasgow Haskell Compiler (\GHC)~\cite{ghc}.
+functions, these descriptions can be compiled to an executable binary
+for simulation using an optimizing Haskell compiler such as the Glasgow
+Haskell Compiler (\GHC)~\cite{ghc}.
Where descriptions in a conventional hardware description language have an
explicit clock for the purpose state and synchronicity, the clock is implied
be converted to an actual netlist format by an (optimizing) \VHDL\ synthesis
tool.
-Besides trivial circuits such as variants of both the FIR filter and the
-simple CPU shown in \Cref{sec:usecases}, the \CLaSH\ compiler has also been
-shown to work for non-trivial descriptions. \CLaSH\ has been able to
+Besides trivial circuits such as variants of both the \acro{FIR} filter and
+the simple \acro{CPU} shown in \Cref{sec:usecases}, the \CLaSH\ compiler has
+also been shown to work for non-trivial descriptions. \CLaSH\ has been able to
successfully translate the functional description of a streaming reduction
circuit~\cite{reductioncircuit} for floating point numbers.
\item function applications are translated to component instantiations.
\end{inparaenum}
The output port can have a structured type (such as a tuple), so having
- just a single output port does not pose any limitation. The arguments of a
- function application are assigned to signals, which are then mapped to
- the corresponding input ports of the component. The output port of the
- function is also mapped to a signal, which is used as the result of the
- application itself.
+ just a single output port does not pose any limitation. The actual
+ arguments of a function application are assigned to signals, which are
+ then mapped to the corresponding input ports of the component. 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 netlist,% aswell,
\end{figure}
\subsection{Choice}
- In Haskell, choice can be achieved by a large set of language constructs,
- consisting of: \hs{case} constructs, \hs{if-then-else} constructs,
+ In Haskell, choice can be achieved by a large set of syntactic elements,
+ consisting of: \hs{case} expressions, \hs{if-then-else} expressions,
pattern matching, and guards. The most general of these are the \hs{case}
- constructs (\hs{if} expressions can be very directly translated to
- \hs{case} expressions). A \hs{case} construct is translated to a
- multiplexer, where the control value is linked to the selection port and
- the output of each case is linked to the corresponding input port on the
- multiplexer.
+ expressions (\hs{if} expressions can be very directly translated to
+ \hs{case} expressions). A \hs{case} expression is translated to a
+ multiplexer, where the control value is fed into a number of
+ comparators and their output is used to compose the selection port
+ of the multiplexer. The result of each alternative is linked to the
+ corresponding input port on the multiplexer.
% A \hs{case} expression can in turn simply be translated to a conditional
% assignment in \VHDL, where the conditions use equality comparisons
% against the constructors in the \hs{case} expressions.
We can see two versions of a contrived example below, the first
- using a \hs{case} construct and the other using an \hs{if-then-else}
- construct, in the code below. The examples sums two values when they are
+ using a \hs{case} expression and the other using an \hs{if-then-else}
+ expression. Both examples sums two values when they are
equal or non-equal (depending on the given predicate, the \hs{pred}
variable) and returns 0 otherwise. The \hs{pred} variable has the
following, user-defined, enumeration datatype:
\begin{code}
- data Pred = Equiv | NotEquiv
+ data Pred = Equal | NotEqual
\end{code}
The naive netlist corresponding to both versions of the example is
\begin{code}
sumif pred a b = case pred of
- Equiv -> case a == b of
+ Equal -> case a == b of
True -> a + b
False -> 0
- NotEquiv -> case a != b of
+ NotEqual -> case a != b of
True -> a + b
False -> 0
\end{code}
\begin{code}
sumif pred a b =
- if pred == Equiv then
+ if pred == Equal then
if a == b then a + b else 0
else
if a != b then a + b else 0
corresponding clause will be used. Expressions can also contain guards,
where the expression is only executed if the guard evaluates to true, and
continues with the next clause if the guard evaluates to false. Like
- \hs{if-then-else} constructs, pattern matching and guards have a
- (straightforward) translation to \hs{case} constructs and can as such be
+ \hs{if-then-else} expressions, pattern matching and guards have a
+ (straightforward) translation to \hs{case} expressions and can as such be
mapped to multiplexers. A third version of the earlier example, using both
pattern matching and guards, can be seen below. The guard is the
expression that follows the vertical bar (\hs{|}) and precedes the
versions of the example.
\begin{code}
- sumif Equiv a b | a == b = a + b
+ sumif Equal a b | a == b = a + b
| otherwise = 0
- sumif NotEquiv a b | a != b = a + b
+ sumif NotEqual a b | a != b = a + b
| otherwise = 0
\end{code}
Haskell is a statically-typed language, meaning that the type of a
variable or function is determined at compile-time. Not all of Haskell's
typing constructs have a clear translation to hardware, this section will
- therefor only deal with the types that do have a clear correspondence
+ therefore only deal with the types that do have a clear correspondence
to hardware. The translatable types are divided into two categories:
\emph{built-in} types and \emph{user-defined} types. Built-in types are
- those types for which a direct translation is defined within the \CLaSH\
- compiler; the term user-defined types should not require any further
- elaboration. The translatable types are also inferable by the compiler,
+ those types for which a fixed translation is defined within the \CLaSH\
+ compiler. The \CLaSH\ compiler has generic translation rules to
+ translated the user-defined types described below.
+
+ The \CLaSH\ compiler is able to infer unspecified types,
meaning that a developer does not have to annotate every function with a
- type signature.
+ type signature (even if it is good practice to do so).
% Translation of two most basic functional concepts has been
% discussed: function application and choice. Before looking further
% using translation rules that are discussed later on.
\subsubsection{Built-in types}
- The following types have direct translations defined within the \CLaSH\
+ The following types have fixed translations defined within the \CLaSH\
compiler:
\begin{xlist}
\item[\bf{Bit}]
% type (where a value of \hs{True} corresponds to a value of
% \hs{High}).
Supporting the Bool type is required in order to support the
- \hs{if-then-else} construct, which requires a \hs{Bool} value for
+ \hs{if-then-else} expression, which requires a \hs{Bool} value for
the condition.
\item[\bf{SizedWord}, \bf{SizedInt}]
these are types to represent integers. A \hs{SizedWord} is unsigned,
arguments: the length of the vector and the type of the elements
contained in it. The short-hand notation used for the vector type in
the rest of paper is: \hs{[a|n]}. Where the \hs{a} is the element
- type, and \hs{n} is the length of the vector.
+ type, and \hs{n} is the length of the vector. Note that this is
+ a notation used in this paper only, vectors are slightly more
+ verbose in real \CLaSH\ descriptions.
% The state type of an 8 element register bank would then for example
% be:
Algebraic datatypes with a single constructor with one or more
fields, are essentially a way to pack a few values together in a
record-like structure. Haskell's built-in tuple types are also defined
- as single constructor algebraic types An example of a single
- constructor type is the following pair of integers:
+ as single constructor algebraic types (but with a bit of
+ syntactic sugar). An example of a single constructor type is the
+ following pair of integers:
\begin{code}
data IntPair = IntPair Int Int
\end{code}
Algebraic datatypes with multiple constructors, but without any
fields are essentially a way to get an enumeration-like type
containing alternatives. Note that Haskell's \hs{Bool} type is also
- defined as an enumeration type, but that there a fixed translation for
- that type within the \CLaSH\ compiler. An example of such an
+ defined as an enumeration type, but that there is a fixed translation
+ for that type within the \CLaSH\ compiler. An example of such an
enumeration type is the type that represents the colors in a traffic
light:
\begin{code}
any number of new types.
As an example of a parametric polymorphic function, consider the type of
- the following \hs{append} function, which appends an element to a vector:
+ the following \hs{append} function, which appends an element to a
+ vector:\footnote{The \hs{::} operator is used to annotate a function
+ with its type.}
\begin{code}
append :: [a|n] -> a -> [a|n + 1]
types that are \emph{instances} of the \emph{type class} \hs{Num}, so that
we know that the addition (+) operator is defined for that type.
\CLaSH's built-in numerical types are also instances of the \hs{Num}
- class, so we can use the addition operator on \hs{SizedWords} as
- well as on \hs{SizedInts}.
+ class, so we can use the addition operator (and thus the \hs{sum}
+ function) with \hs{SizedWords} as well as with \hs{SizedInts}.
In \CLaSH, parametric 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 argument depending on how the
function is applied. There is however one constraint: the top level
function that is being translated can not have any polymorphic arguments.
- The arguments can not be polymorphic as they are never applied and
+ The arguments can not be polymorphic as the function is never applied and
consequently there is no way to determine the actual types for the type
parameters.
The \hs{map} function is called a higher-order function, since it takes
another function as an argument. Also note that \hs{map} is again a
parametric polymorphic function: it does not pose any constraints on the
- type of the vector elements, other than that it must be the same type as
- the input type of the function passed to \hs{map}. The element type of the
- resulting vector is equal to the return type of the function passed, which
- need not necessarily be the same as the element type of the input vector.
- All of these characteristics can readily be inferred from the type
- signature belonging to \hs{map}:
+ type of the input vector, other than that its elements must have the same
+ type as the first argument of the function passed to \hs{map}. The element
+ type of the resulting vector is equal to the return type of the function
+ passed, which need not necessarily be the same as the element type of the
+ input vector. All of these characteristics can readily be inferred from
+ the type signature belonging to \hs{map}:
\begin{code}
map :: (a -> b) -> [a|n] -> [b|n]
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 lambda expression allows one to introduce an
- anonymous function in any expression. Consider the following expression,
- which again adds one to every element of a vector:
+ adds one to its (next) argument. A lambda expression allows one to
+ introduce an anonymous function in any expression. Consider the following
+ expression, which again adds one to every element of a vector:
\begin{code}
map (\x -> x + 1) xs
\end{code}
- Finally, higher order arguments are not limited to just built-in
- functions, but any function defined by a developer can have function
+ Finally, not only built-in functions can have higher order
+ arguments, 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. The only exception is again the top-level function: if a
stateful design, the outputs depend on the history of the inputs, or the
state. State is usually stored in registers, which retain their value
during a clock cycle. As we want to describe more than simple
- combinatorial designs, \CLaSH\ needs an abstraction mechanism for state.
+ combinational designs, \CLaSH\ needs an abstraction mechanism for state.
An important property in Haskell, and in most other functional languages,
is \emph{purity}. A function is said to be \emph{pure} if it satisfies two
% This purity property is important for functional languages, since it
% enables all kinds of mathematical reasoning that could not be guaranteed
% correct for impure functions.
- Pure functions are as such a perfect match for combinatorial circuits,
+ Pure functions are as such a perfect match for combinational circuits,
where the output solely depends on the inputs. When a circuit has state
however, it can no longer be simply described by a pure function.
% Simply removing the purity property is not a valid option, as the
In \CLaSH\ we deal with the concept of state in pure functions by making
current value of the state an additional argument of the function and the
updated state part of result. In this sense the descriptions made in
- \CLaSH\ are the combinatorial parts of a mealy machine.
+ \CLaSH\ are the combinational parts of a mealy machine.
A simple example is adding an accumulator register to the earlier
multiply-accumulate circuit, of which the resulting netlist can be seen in
which variables are part of the state is completely determined by the
type signature. This approach to state is well suited to be used in
combination with the existing code and language features, such as all the
- choice constructs, as state values are just normal values. We can simulate
+ choice elements, as state values are just normal values. We can simulate
stateful descriptions using the recursive \hs{run} function:
\begin{code}
bonus in case there is a large set of test inputs.
\section{\CLaSH\ compiler}
-An important aspect in this research is the creation of the prototype compiler, which allows us to translate descriptions made in the \CLaSH\ language as described in the previous section to synthesizable \VHDL, allowing a designer to actually run a \CLaSH\ design on an \acro{FPGA}.
-
-The Glasgow Haskell Compiler (\GHC) is an open-source Haskell compiler that also provides a high level API to most of its internals. The availability of this high-level API obviated the need to design many of the tedious parts of the prototype compiler, such as the parser, semantic checker, and especially the type-checker. The parser, semantic checker, and type-checker together form the front-end of the prototype compiler pipeline, as depicted in \Cref{img:compilerpipeline}.
+An important aspect in this research is the creation of the prototype
+compiler, which allows us to translate descriptions made in the \CLaSH\
+language as described in the previous section to synthesizable \VHDL, allowing
+a designer to actually run a \CLaSH\ design on an \acro{FPGA}.
+
+The Glasgow Haskell Compiler (\GHC) is an open-source Haskell compiler that
+also provides a high level API to most of its internals. The availability of
+this high-level API obviated the need to design many of the tedious parts of
+the prototype compiler, such as the parser, semantic checker, and especially
+the type-checker. The parser, semantic checker, and type-checker together form
+the front-end of the prototype compiler pipeline, as depicted in
+\Cref{img:compilerpipeline}.
\begin{figure}
\centerline{\includegraphics{compilerpipeline.svg}}
\label{img:compilerpipeline}
\end{figure}
-The output of the \GHC\ front-end is the original Haskell description translated to \emph{Core}~\cite{Sulzmann2007}, which is smaller, functional, typed language that is relatively easier to process than the larger Haskell language. A description in \emph{Core} can still contain properties which have no direct translation to hardware, such as polymorphic types and function-valued arguments. Such a description needs to be transformed to a \emph{normal form}, which only contains properties that have a direct translation. The second stage of the compiler, the \emph{normalization} phase exhaustively applies a set of \emph{meaning-preserving} transformations on the \emph{Core} description until this description is in a \emph{normal form}. This set of transformations includes transformations typically found in reduction systems for lambda calculus, such a $\beta$-reduction and $\eta$-expansion, but also includes \emph{defunctionalization} transformations which reduce higher-order functions to `regular' first-order functions.
-
-The final step in the compiler pipeline is the translation to a \VHDL\ \emph{netlist}, which is a straightforward process due to resemblance of a normalized description and a set of concurrent signal assignments. We call the end-product of the \CLaSH\ compiler a \VHDL\ \emph{netlist} as the resulting \VHDL\ resembles an actual netlist description and not idiomatic \VHDL.
+The output of the \GHC\ front-end is the original Haskell description
+translated to \emph{Core}~\cite{Sulzmann2007}, which is smaller, typed,
+functional language that is relatively easier to process than the larger
+Haskell language. A description in \emph{Core} can still contain properties
+which have no direct translation to hardware, such as polymorphic types and
+function-valued arguments. Such a description needs to be transformed to a
+\emph{normal form}, which only contains properties that have a direct
+translation. The second stage of the compiler, the \emph{normalization} phase,
+exhaustively applies a set of \emph{meaning-preserving} transformations on the
+\emph{Core} description until this description is in a \emph{normal form}.
+This set of transformations includes transformations typically found in
+reduction systems for lambda calculus~\cite{lambdacalculus}, such a
+$\beta$-reduction and $\eta$-expansion, but also includes self-defined
+transformations that are responsible for the reduction of higher-order
+functions to `regular' first-order functions.
+
+The final step in the compiler pipeline is the translation to a \VHDL\
+\emph{netlist}, which is a straightforward process due to resemblance of a
+normalized description and a set of concurrent signal assignments. We call the
+end-product of the \CLaSH\ compiler a \VHDL\ \emph{netlist} as the resulting
+\VHDL\ resembles an actual netlist description and not idiomatic \VHDL.
\section{Use cases}
\label{sec:usecases}
+\subsection{FIR Filter}
As an example of a common hardware design where the use of higher-order
-functions leads to a very natural description is a FIR filter, which is
+functions leads to a very natural description is a \acro{FIR} filter, which is
basically the dot-product of two vectors:
\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
+A \acro{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 $t$. The equation of a FIR
+are summed, to produce the result at time $t$. The equation of a \acro{FIR}
filter is indeed equivalent to the equation of the dot-product, which is
shown below:
\begin{equation}
-\mathbf{x}\bullet\mathbf{y} = \sum\nolimits_{i = 0}^{n - 1} {x_i \cdot y_i }
+\mathbf{a}\bullet\mathbf{b} = \sum\nolimits_{i = 0}^{n - 1} {a_i \cdot b_i }
\end{equation}
We can easily and directly implement the equation for the dot-product
using higher-order functions:
\begin{code}
-xs *+* ys = foldl1 (+) (zipWith (*) xs hs)
+as *+* bs = foldl1 (+) (zipWith (*) as bs)
\end{code}
The \hs{zipWith} function is very similar to the \hs{map} function seen
earlier: It takes a function, two vectors, and then applies the function to
each of the elements in the two vectors pairwise (\emph{e.g.}, \hs{zipWith (*)
-[1, 2] [3, 4]} becomes \hs{[1 * 3, 2 * 4]} $\equiv$ \hs{[3,8]}).
+[1, 2] [3, 4]} becomes \hs{[1 * 3, 2 * 4]}).
-The \hs{foldl1} function takes a function, a single vector, and applies
+The \hs{foldl1} function takes a binary function, a single vector, and applies
the function to the first two elements of the vector. It then applies the
-function to the result of the first application and the next element from
-the vector. This continues until the end of the vector 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.
-
-Returning to the actual FIR filter, we will slightly change the
-equation belong to it, so as to make the translation to code more obvious.
-What we will do is change the definition of the vector of input samples.
-So, instead of having the input sample received at time
-$t$ stored 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 will better suit
-the transformation to code):
+function to the result of the first application and the next element in the
+vector. This continues until the end of the vector is reached. The result of
+the \hs{foldl1} function is the result of the last application. It is obvious
+that the \hs{zipWith (*)} function is pairwise multiplication and that the
+\hs{foldl1 (+)} function is summation.
+
+Returning to the actual \acro{FIR} filter, we will slightly change the
+equation describing it, so as to make the translation to code more obvious and
+concise. What we do is change the definition of the vector of input samples
+and delay the computation by one sample. Instead of having the input sample
+received at time $t$ stored in $x_t$, $x_0$ now always stores the newest
+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 will better suit the
+transformation to code):
\begin{equation}
y_t = \sum\nolimits_{i = 0}^{n - 1} {x_i \cdot h_i }
\end{equation}
-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. The function that shifts the input samples is shown below:
+The complete definition of the \acro{FIR} filter in code then becomes:
\begin{code}
-x >> xs = x +> tail xs
+fir (State (xs,hs)) x = (State (x >> xs,hs), xs *+* hs)
\end{code}
-Where the \hs{tail} function returns all but the first element of a
-vector, and the concatenate operator ($\succ$) adds a new element to the
-left of a vector. The complete definition of the FIR filter then becomes:
+Where the vector \hs{hs} contains the \acro{FIR} coefficients and the vector
+\hs{xs} contains the previous input sample in front and older samples behind.
+The code for the shift (\hs{>>}) operator, that adds the new input sample
+(\hs{x}) to the list of previous input samples (\hs{xs}) and removes the
+oldest sample, is shown below:
\begin{code}
-fir (State (xs,hs)) x = (State (x >> xs,hs), xs *+* hs)
+x >> xs = x +> init xs
\end{code}
-The resulting netlist of a 4-taps FIR filter based on the above definition
-is depicted in \Cref{img:4tapfir}.
+The \hs{init} function returns all but the last element of a vector, and the
+concatenate operator (\hs{+>}) adds a new element to the front of a vector.
+The resulting netlist of a 4-taps \acro{FIR} filter, created by specializing
+the vectors of the \acro{FIR} code to a length of 4, is depicted in
+\Cref{img:4tapfir}.
\begin{figure}
\centerline{\includegraphics{4tapfir.svg}}
\label{img:4tapfir}
\end{figure}
-
\subsection{Higher order CPU}
-
\begin{code}
-type FuState = State Word
-fu :: (a -> a -> a)
- -> [a]:n
- -> (RangedWord n, RangedWord n)
- -> FuState
- -> (FuState, a)
-fu op inputs (addr1, addr2) (State out) =
- (State out', out)
+fu op inputs (addr1, addr2) = regIn
where
- in1 = inputs!addr1
- in2 = inputs!addr2
- out' = op in1 in2
+ in1 = inputs!addr1
+ in2 = inputs!addr2
+ regIn = op in1 in2
\end{code}
\begin{code}
-type CpuState = State [FuState]:4
-cpu :: Word
- -> [(RangedWord 7, RangedWord 7)]:4
- -> CpuState
- -> (CpuState, Word)
-cpu input addrs (State fuss) =
- (State fuss', out)
+cpu :: Word -> [(Index 6, Index 6) | 4]
+ -> State [Word | 4] -> (State [Word | 4], Word)
+cpu input addrs (State fuss) = (State fuss', out)
where
- fures = [ fu const inputs!0 fuss!0
- , fu (+) inputs!1 fuss!1
- , fu (-) inputs!2 fuss!2
- , fu (*) inputs!3 fuss!3
- ]
- (fuss', outputs) = unzip fures
- inputs = 0 +> 1 +> input +> outputs
- out = head outputs
+ fuss' = [ fu const inputs (addrs!0) (fuss!0)
+ , fu (+) inputs (addrs!1) (fuss!1)
+ , fu (-) inputs (addrs!2) (fuss!2)
+ , fu (*) inputs (addrs!3) (fuss!3)
+ ]
+ inputs = 0 +> (1 +> (input +> fuss))
+ out = head fuss
\end{code}
\section{Related work}
+This section describes the features of existing (functional) hardware
+description languages and highlights the advantages that this research has
+over existing work.
+
Many functional hardware description languages have been developed over the
years. Early work includes such languages as $\mu$\acro{FP}~\cite{muFP}, an
extension of Backus' \acro{FP} language to synchronous streams, designed
functional language \acro{ML}, and has support for polymorphic types and
higher-order functions. Published work suggests that there is no direct
simulation support for \acro{HML}, but that a description in \acro{HML} has to
-be translated to \VHDL\ and that the translated description can than be
+be translated to \VHDL\ and that the translated description can then be
simulated in a \VHDL\ simulator. Also not all of the mentioned language
features of \acro{HML} could be translated to hardware. The \CLaSH\ compiler
on the other hand can correctly translate all of the language constructs
The merits of polymorphic typing, combined with higher-order functions, are
now also recognized in the `main-stream' hardware description languages,
-exemplified by the new \VHDL-2008 standard~\cite{VHDL2008}. \VHDL-2008 support for generics has been extended to types, allowing a developer to describe
-polymorphic components. Note that those types still require an explicit
-generic map, whereas types can be automatically inferred in \CLaSH.
+exemplified by the new \VHDL-2008 standard~\cite{VHDL2008}. \VHDL-2008 support
+for generics has been extended to types and subprograms, allowing a developer to describe components with polymorphic ports and function-valued arguments. Note that the types and subprograms still require an explicit generic map, whereas types can be automatically inferred, and function-values can be automatically propagated by the \CLaSH\ compiler. There are also no (generally available) \VHDL\ synthesis tools that currently support the \VHDL-2008 standard, and thus the synthesis of polymorphic types and function-valued arguments.
% Wired~\cite{Wired},, T-Ruby~\cite{T-Ruby}, Hydra~\cite{Hydra}.
%
% use section* for acknowledgement
-\section*{Acknowledgment}
-
-
-The authors would like to thank...
-
-
-
-
+% \section*{Acknowledgment}
+%
+% The authors would like to thank...
% trigger a \newpage just before the given reference
% number - used to balance the columns on the last page
projects / matthijs / master-project / dsd-paper.git / blobdiff