X-Git-Url: https://git.stderr.nl/gitweb?p=matthijs%2Fmaster-project%2Freport.git;a=blobdiff_plain;f=Chapters%2FHardwareDescription.tex;h=508c11bef4a4dd3f5028ae08cf7befc99a7c4d56;hp=c9005967fd128fadfed7f712227d3939010d37f7;hb=8515f491f83e63caa71de03c6e71b25df12aedb2;hpb=6b779650796b6ef5c72ea261238f8576b049d925 diff --git a/Chapters/HardwareDescription.tex b/Chapters/HardwareDescription.tex index c900596..508c11b 100644 --- a/Chapters/HardwareDescription.tex +++ b/Chapters/HardwareDescription.tex @@ -18,7 +18,7 @@ \section{Function application} The basic syntactic element of a functional program are functions and - function application. These have a single obvious VHDL translation: Each + function application. These have a single obvious \small{VHDL} translation: Each function becomes a hardware component, where each argument is an input port and the result value is the output port. @@ -27,17 +27,54 @@ 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. - An example of a simple program using only function application would be: + \in{Example}[ex:And3] shows a simple program using only function + application and the corresponding architecture. - \starthaskell - -- | A simple function that returns the and of three bits - and3 :: Bit -> Bit -> Bit -> Bit - and3 a b c = and (and a b) c - \stophaskell +\startbuffer[And3] +-- | A simple function that returns +-- the and of three bits +and3 :: Bit -> Bit -> Bit -> Bit +and3 a b c = and (and a b) c +\stopbuffer - This results in the following hardware: - - TODO: Pretty picture + \startuseMPgraphic{And3} + save a, b, c, anda, andb, out; + + % I/O ports + newCircle.a(btex $a$ etex) "framed(false)"; + newCircle.b(btex $b$ etex) "framed(false)"; + newCircle.c(btex $c$ etex) "framed(false)"; + newCircle.out(btex $out$ etex) "framed(false)"; + + % Components + newCircle.anda(btex $and$ etex); + newCircle.andb(btex $and$ etex); + + a.c = origin; + b.c = a.c + (0cm, 1cm); + c.c = b.c + (0cm, 1cm); + anda.c = midpoint(a.c, b.c) + (2cm, 0cm); + andb.c = midpoint(b.c, c.c) + (4cm, 0cm); + + out.c = andb.c + (2cm, 0cm); + + % Draw objects and lines + drawObj(a, b, c, anda, andb, out); + + ncarc(a)(anda) "arcangle(-10)"; + ncarc(b)(anda); + ncarc(anda)(andb); + ncarc(c)(andb); + ncline(andb)(out); + \stopuseMPgraphic + + \placeexample[here][ex:And3]{Simple three port and.} + \startcombination[2*1] + {\typebufferhs{And3}}{Haskell description using function applications.} + {\boxedgraphic{And3}}{The architecture described by the Haskell description.} + \stopcombination + + TODO: Define top level function and subfunctions/circuits. \subsection{Partial application} It should be obvious that we cannot generate hardware signals for all @@ -47,19 +84,50 @@ represented as a signal or i/o port to a component. From this, we can see that the above translation rules do not apply to a - partial application. Let's look at an example: + partial application. \in{Example}[ex:Quadruple] shows an example use of + partial application and the corresponding architecture. - \starthaskell - -- | Multiply the input word by four. - quadruple :: Word -> Word - quadruple n = mul (mul n) - where - mul = (*) 2 - \stophaskell +\startbuffer[Quadruple] +-- | Multiply the input word by four. +quadruple :: Word -> Word +quadruple n = mul (mul n) + where + mul = (*) 2 +\stopbuffer + + \startuseMPgraphic{Quadruple} + save in, two, mula, mulb, out; + + % I/O ports + newCircle.in(btex $n$ etex) "framed(false)"; + newCircle.two(btex $2$ etex) "framed(false)"; + newCircle.out(btex $out$ etex) "framed(false)"; + + % Components + newCircle.mula(btex $\times$ etex); + newCircle.mulb(btex $\times$ etex); - It should be clear that the above code describes the following hardware: + two.c = origin; + in.c = two.c + (0cm, 1cm); + mula.c = in.c + (2cm, 0cm); + mulb.c = mula.c + (2cm, 0cm); + out.c = mulb.c + (2cm, 0cm); - TODO: Pretty picture + % Draw objects and lines + drawObj(in, two, mula, mulb, out); + + nccurve(two)(mula) "angleA(0)", "angleB(45)"; + nccurve(two)(mulb) "angleA(0)", "angleB(45)"; + ncline(in)(mula); + ncline(mula)(mulb); + ncline(mulb)(out); + \stopuseMPgraphic + + \placeexample[here][ex:Quadruple]{Simple three port and.} + \startcombination[2*1] + {\typebufferhs{Quadruple}}{Haskell description using function applications.} + {\boxedgraphic{Quadruple}}{The architecture described by the Haskell description.} + \stopcombination Here, the definition of mul is a partial function application: It applies \hs{2 :: Word} to the function \hs{(*) :: Word -> Word -> Word} resulting in @@ -76,7 +144,395 @@ function boundaries), but eventually, the partial application will become completely applied. - \section{Recursion} + \section{Costless specialization} + Each (complete) function application in our description generates a + component instantiation, or a specific piece of hardware in the final + design. It is interesting to note that each application of a function + generates a \emph{separate} piece of hardware. In the final design, none + of the hardware is shared between applications, even when the applied + function is the same. + + This is distinctly different from normal program compilation: Two separate + calls to the same function share the same machine code. Having more + machine code has implications for speed (due to less efficient caching) + and memory usage. For normal compilation, it is therefore important to + keep the amount of functions limited and maximize the code sharing. + + When generating hardware, this is hardly an issue. Having more \quote{code + sharing} does reduce the amount of \small{VHDL} output (Since different + component instantiations still share the same component), but after + synthesis, the amount of hardware generated is not affected. + + In particular, if we would duplicate all functions so that there is a + duplicate for every application in the program (\eg, each function is then + only applied exactly once), there would be no increase in hardware size + whatsoever. + + TODO: Perhaps these next two sections are a bit too + implementation-oriented? + + \subsection{Specialization} + Because of this, a common optimization technique called + \emph{specialization} is as good as free for hardware generation. Given + some function that has a \emph{domain} of $D$ (\eg, the set of all + possible arguments that could be applied), we create a specialized + function with exactly the same behaviour, but with an domain of $D' + \subset D$. This subset can be derived in all sort of ways, but commonly + this is done by limiting a polymorphic argument to a single type (\eg, + removing polymorphism) or by limiting an argument to just a single value + (\eg, cross-function constant propagation, effectively removing the + argument). + + Since we limit the argument domain of the specialized function, its + definition can often be optimized further (since now more types or even + values of arguments are already know). By replacing any application of + the function that falls within the reduced domain by an application of + the specialized version, the code gets faster (but the code also gets + bigger, since we now have two versions instead of one!). If we apply + this technique often enough, we can often replace all applications of a + function by specialized versions, allowing the original function to be + removed (in some cases, this can even give a net reduction of the code + compared to the non-specialized version). + + Specialization is useful for our hardware descriptions for functions + that contain arguments that cannot be translated to hardware directly + (polymorphic or higher order arguments, for example). If we can create + specialized functions that remove the argument, or make it translatable, + we can use specialization to make the original, untranslatable, function + obsolete. + + \section{Higher order values} + What holds for partial application, can be easily generalized to any + higher order expression. This includes partial applications, plain + variables (e.g., a binder referring to a top level function), lambda + expressions and more complex expressions with a function type (a case + expression returning lambda's, for example). + + Each of these values cannot be directly represented in hardware (just like + partial applications). Also, to make them representable, they need to be + applied: function variables and partial applications will then eventually + become complete applications, applied lambda expressions disappear by + applying β-reduction, etc. + + So any higher order value will be \quote{pushed down} towards its + application just like partial applications. Whenever a function boundary + needs to be crossed, the called function can be specialized. + + TODO: This is section should be improved + + \section{Polymorphism} + In Haskell, values can be polymorphic: They can have multiple types. For + example, the function \hs{fst :: (a, b) -> a} is an example of a + polymorphic function: It works for tuples with any element types. Haskell + typeclasses allow a function to work on a specific set of types, but the + general idea is the same. + +% A type class is a collection of types for which some operations are +% defined. It is thus possible for a value to be polymorphic while having +% any number of \emph{class constraints}: The value is not defined for +% every type, but only for types in the type class. An example of this is +% the \hs{even :: (Integral a) => a -> Bool} function, which can map any +% value of a type that is member of the \hs{Integral} type class + + When generating hardware, polymorphism can't be easily translated. How + many wire will you lay down for a value that could have any type? When + type classes are involved, what hardware components will you lay down for + a class method (whose behaviour depends on the type of its arguments)? + + Fortunately, we can again use the principle of specialization: Since every + function application generates separate pieces of hardware, we can know + the types of all arguments exactly. Provided that we don't use existential + typing, all of the polymorphic types in a function must depend on the + types of the arguments (In other words, the only way to introduce a type + variable is in a lambda abstraction). Our top level function must not have + a polymorphic type (otherwise we wouldn't know the hardware interface to + our top level function). + + If a function is monomorphic, all values inside it are monomorphic as + well, so any function that is applied within the function can only be + applied to monomorphic values. The applied functions can then be + specialized to work just for these specific types, removing the + polymorphism from the applied functions as well. + + By induction, this means that all functions that are (indirectly) called + by our top level function (meaning all functions that are translated in + the final hardware) become monomorphic. + + \section{State} + A very important concept in hardware designs is \emph{state}. In a + stateless (or, \emph{combinatoric}) design, every output is a directly and solely dependent on the + inputs. In a stateful design, the outputs can depend on the history of + inputs, or the \emph{state}. State is usually stored in \emph{registers}, + which retain their value during a clockcycle, and are typically updated at + the start of every clockcycle. Since the updating of the state is tightly + coupled (synchronized) to the clock signal, these state updates are often + called \emph{synchronous}. + + To make our hardware description language useful to describe more that + simple combinatoric designs, we'll need to be able to describe state in + some way. + + \subsection{Approaches to state} + In Haskell, functions are always pure (except when using unsafe + functions like \hs{unsafePerformIO}, which should be prevented whenever + possible). This means that the output of a function solely depends on + its inputs. If you evaluate a given function with given inputs, it will + always provide the same output. + + TODO: Define pure + + This is a perfect match for a combinatoric circuit, where the output + also soley depend on the inputs. However, when state is involved, this + no longer holds. Since we're in charge of our own language, we could + remove this purity constraint and allow a function to return different + values depending on the cycle in which it is evaluated (or rather, the + current state). However, this means that all kinds of interesting + properties of our functional language get lost, and all kinds of + transformations and optimizations might no longer be meaning preserving. + + Provided that we want to keep the function pure, the current state has + to be present in the function's arguments in some way. There seem to be + two obvious ways to do this: Adding the current state as an argument, or + including the full history of each argument. + + \subsubsection{Stream arguments and results} + Including the entire history of each input (\eg, the value of that + input for each previous clockcycle) is an obvious way to make outputs + depend on all previous input. This is easily done by making every + input a list instead of a single value, containing all previous values + as well as the current value. + + An obvious downside of this solution is that on each cycle, all the + previous cycles must be resimulated to obtain the current state. To do + this, it might be needed to have a recursive helper function as well, + wich might be hard to properly analyze by the compiler. + + A slight variation on this approach is one taken by some of the other + functional \small{HDL}s in the field (TODO: References to Lava, + ForSyDe, ...): Make functions operate on complete streams. This means + that a function is no longer called on every cycle, but just once. It + takes stream as inputs instead of values, where each stream contains + all the values for every clockcycle since system start. This is easily + modeled using an (infinite) list, with one element for each clock + cycle. Since the funciton is only evaluated once, its output is also a + stream. Note that, since we are working with infinite lists and still + want to be able to simulate the system cycle-by-cycle, this relies + heavily on the lazy semantics of Haskell. + + Since our inputs and outputs are streams, all other (intermediate) + values must be streams. All of our primitive operators (\eg, addition, + substraction, bitwise operations, etc.) must operate on streams as + well (note that changing a single-element operation to a stream + operation can done with \hs{map}, \hs{zipwith}, etc.). + + Note that the concept of \emph{state} is no more than having some way + to communicate a value from one cycle to the next. By introducing a + \hs{delay} function, we can do exactly that: Delay (each value in) a + stream so that we can "look into" the past. This \hs{delay} function + simply outputs a stream where each value is the same as the input + value, but shifted one cycle. This causes a \quote{gap} at the + beginning of the stream: What is the value of the delay output in the + first cycle? For this, the \hs{delay} function has a second input + (which is a value, not a stream!). + + \in{Example}[ex:DelayAcc] shows a simple accumulator expressed in this + style. + +\startbuffer[DelayAcc] +acc :: Stream Word -> Stream Word +acc in = out + where + out = (delay out 0) + in +\stopbuffer + +\startuseMPgraphic{DelayAcc} + save in, out, add, reg; + + % I/O ports + newCircle.in(btex $in$ etex) "framed(false)"; + newCircle.out(btex $out$ etex) "framed(false)"; + + % Components + newReg.reg("") "dx(4mm)", "dy(6mm)", "reflect(true)"; + newCircle.add(btex + etex); + + in.c = origin; + add.c = in.c + (2cm, 0cm); + out.c = add.c + (2cm, 0cm); + reg.c = add.c + (0cm, 2cm); + + % Draw objects and lines + drawObj(in, out, add, reg); + + nccurve(add)(reg) "angleA(0)", "angleB(180)", "posB(d)"; + nccurve(reg)(add) "angleA(180)", "angleB(-45)", "posA(out)"; + ncline(in)(add); + ncline(add)(out); +\stopuseMPgraphic + + + \placeexample[here][ex:DelayAcc]{Simple accumulator architecture.} + \startcombination[2*1] + {\typebufferhs{DelayAcc}}{Haskell description using streams.} + {\boxedgraphic{DelayAcc}}{The architecture described by the Haskell description.} + \stopcombination + + + This notation can be confusing (especially due to the loop in the + definition of out), but is essentially easy to interpret. There is a + single call to delay, resulting in a circuit with a single register, + whose input is connected to \hs{outl (which is the output of the + adder)}, and it's output is the \hs{delay out 0} (which is connected + to one of the adder inputs). + + This notation has a number of downsides, amongst which are limited + readability and ambiguity in the interpretation. TODO: Reference + Christiaan. + + \subsubsection{Explicit state arguments and results} + A more explicit way to model state, is to simply add an extra argument + containing the current state value. This allows an output to depend on + both the inputs as well as the current state while keeping the + function pure (letting the result depend only on the arguments), since + the current state is now an argument. + + In Haskell, this would look like \in{example}[ex:ExplicitAcc]. + +\startbuffer[ExplicitAcc] +-- input -> current state -> (new state, output) +acc :: Word -> Word -> (Word, Word) +acc in (State s) = (State s', out) + where + out = s + in + s' = out +\stopbuffer + + \placeexample[here][ex:ExplicitAcc]{Simple accumulator architecture.} + \startcombination[2*1] + {\typebufferhs{ExplicitAcc}}{Haskell description using explicit state arguments.} + % Picture is identical to the one we had just now. + {\boxedgraphic{DelayAcc}}{The architecture described by the Haskell description.} + \stopcombination + + This approach makes a function's state very explicit, which state + variables are used by a function can be completely determined from its + type signature (as opposed to the stream approach, where a function + looks the same from the outside, regardless of what state variables it + uses (or wether it's stateful at all). + + This approach is the one chosen for Cλash and will be examined more + closely below. + + \subsection{Explicit state specification} + We've seen the concept of explicit state in a simple example below, but + what are the implications of this approach? + + \subsubsection{Substates} + Since a function's state is reflected directly in its type signature, + if a function calls other stateful functions (\eg, has subcircuits) it + has to somehow know the current state for these called functions. The + only way to do this, is to put these \emph{substates} inside the + caller's state. This means that a function's state is the sum of the + states of all functions it calls, and its own state. + + This also means that the type of a function (at least the "state" + part) is dependent on its implementation and the functions it calls. + This is the major downside of this approach: The separation between + interface and implementation is limited. However, since Cλash is not + very suitable for separate compilation (see + \in{section}[sec:prototype:separate]) this is not a big problem in + practice. Additionally, when using a type synonym for the state type + of each function, we can still provide explicit type signatures + while keeping the state specification for a function near its + definition only. + + \subsubsection{...} + We need some way to know which arguments should become input ports and + which argument(s?) should become the current state (\eg, be bound to + the register outputs). This does not hold holds not just for the top + level function, but also for any subfunctions. Or could we perhaps + deduce the statefulness of subfunctions by analyzing the flow of data + in the calling functions? + + To explore this matter, we make an interesting observation: We get + completely correct behaviour when we put all state registers in the + top level entity (or even outside of it). All of the state arguments + and results on subfunctions are treated as normal input and output + ports. Effectively, a stateful function results in a stateless + hardware component that has one of its input ports connected to the + output of a register and one of its output ports connected to the + input of the same register. + + TODO: Example? + + Of course, even though the hardware described like this has the + correct behaviour, unless the layout tool does smart optimizations, + there will be a lot of extra wire in the design (since registers will + not be close to the component that uses them). Also, when working with + the generated \small{VHDL} code, there will be a lot of extra ports + just to pass one state values, which can get quite confusing. + + To fix this, we can simply \quote{push} the registers down into the + subcircuits. When we see a register that is connected directly to a + subcircuit, we remove the corresponding input and output port and put + the register inside the subcircuit instead. This is slightly less + trivial when looking at the Haskell code instead of the resulting + circuit, but the idea is still the same. + + TODO: Example? + + However, when applying this technique, we might push registers down + too far. When you intend to store a result of a stateless subfunction + in the caller's state and pass the current value of that state + variable to that same function, the register might get pushed down too + far. It is impossible to distinguish this case from similar code where + the called function is in fact stateful. From this we can conclude + that we have to either: + + \startitemize + \item accept that the generated hardware might not be exactly what we + intended, in some specific cases. In most cases, the hardware will be + what we intended. + \item explicitely annotate state arguments and results in the input + description. + \stopitemize + + The first option causes (non-obvious) exceptions in the language + intepretation. Also, automatically determining where registers should + end up is easier to implement correctly with explicit annotations, so + for these reasons we will look at how this annotations could work. + + + TODO: Note about conditions on state variables and checking them. + + \subsection{Explicit state annotation} + To make our stateful descriptions unambigious and easier to translate, + we need some way for the developer to describe which arguments and + results are intended to become stateful. + + Roughly, we have two ways to achieve this: + \startitemize[KR] + \item Use some kind of annotation method or syntactic construction in + the language to indicate exactly which argument and (part of the) + result is stateful. This means that the annotation lives + \quote{outside} of the function, it is completely invisible when + looking at the function body. + \item Use some kind of annotation on the type level, \eg give stateful + arguments and (part of) results a different type. This has the + potential to make this annotation visible inside the function as well, + such that when looking at a value inside the function body you can + tell if it's stateful by looking at its type. This could possibly make + the translation process a lot easier, since less analysis of the + program flow might be required. + \stopitemize + + From these approaches, the type level \quote{annotations} have been + implemented in Cλash. \in{Section}[sec:prototype:statetype] expands on + the possible ways this could have been implemented. + + TODO: Say something about dependent types and fixed size vectors + + \section[sec:recursion]{Recursion} An import concept in functional languages is recursion. In it's most basic form, recursion is a function that is defined in terms of itself. This usually requires multiple evaluations of this function, with changing @@ -177,3 +633,6 @@ Due to these complications, we leave other forms of recursion as future work as well. + + \section{Supported types} + TODO