X-Git-Url: https://git.stderr.nl/gitweb?p=matthijs%2Fmaster-project%2Fdsd-paper.git;a=blobdiff_plain;f=c%CE%BBash.lhs;h=2adeed6e903eaf48d805917ad38a44e8f94de739;hp=2c5ad89aa06c0c03bfc98d40ba2a70a390ad6d84;hb=b28d9dd4fcf8b751765edd32494b2e41f9ff5578;hpb=1eade9ec370918d224ec5acb43fc5a428ce7fb94

diff --git "a/c\316\273ash.lhs" "b/c\316\273ash.lhs"
index 2c5ad89..2adeed6 100644
--- "a/c\316\273ash.lhs"
+++ "b/c\316\273ash.lhs"
@@ -907,7 +907,7 @@ by an (optimizing) \VHDL\ synthesis tool.
     
     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 $t$. The equation of the FIR 
+    are summed, to produce the result at time $t$. The equation of a FIR 
     filter is indeed equivalent to the equation of the dot-product, which is 
     shown below:
     
@@ -1021,50 +1021,68 @@ by an (optimizing) \VHDL\ synthesis tool.
     combination with the existing code and language features, such as all the 
     choice constructs, as state values are just normal values.
     
-    Returning to the example of the 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 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 does this shifting of the input samples 
-    is shown below:
+    We can simulate stateful descriptions using the recursive \hs{run} 
+    function:
     
     \begin{code}
-    x >> xs = x +> tail xs  
-    \end{code}
-    
-    Where the \hs{tail} functions returns all but the first element of a 
-    vector, and the concatenate operator ($\succ$) adds the new element to the 
-    left of a vector. The complete definition of the FIR filter then becomes:
-    
-    \begin{code}
-    fir (State (xs,hs)) x = (State (x >> xs,hs), xs *+* hs)
+    run f s (i:inps) = o : (run f s' inps)
+      where
+        (s', o) = f s i
     \end{code}
     
-    The resulting netlist of a 4-taps FIR filter based on the above definition
-    is depicted in \Cref{img:4tapfir}.
-    
-    \begin{figure}
-    \centerline{\includegraphics{4tapfir}}
-    \caption{4-taps FIR Filter}
-    \label{img:4tapfir}
-    \end{figure}
+    The \hs{run} function maps a list of inputs over the function that a 
+    developer wants to simulate, passing the state to each new iteration. Each
+    value in the input list corresponds to exactly one cycle of the (implicit) 
+    clock. The result of the simulation is a list of outputs for every clock
+    cycle. As both the \hs{run} function and the hardware description are 
+    plain hardware, the complete simulation can be compiled by an optimizing
+    Haskell compiler.
     
 \section{\CLaSH\ prototype}
 
 foo\par bar
 
+\section{Use cases}
+Returning to the example of the 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 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 does this shifting of the input samples 
+is shown below:
+
+\begin{code}
+x >> xs = x +> tail xs  
+\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:
+
+\begin{code}
+fir (State (xs,hs)) x = (State (x >> xs,hs), xs *+* hs)
+\end{code}
+
+The resulting netlist of a 4-taps FIR filter based on the above definition
+is depicted in \Cref{img:4tapfir}.
+
+\begin{figure}
+\centerline{\includegraphics{4tapfir}}
+\caption{4-taps FIR Filter}
+\label{img:4tapfir}
+\end{figure}
+
 \section{Related work}
 Many functional hardware description languages have been developed over the 
 years. Early work includes such languages as $\mu$\acro{FP}~\cite{muFP}, an