m1-internship-report/report/report.tex

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\title{Pattern-matching and substitution in electronic circuits}
\author{Théophile Bastian, under supervision of Carl-Johan Seger
        and Mary Sheeran\\
    \small{Chalmers University, Göteborg, Sweden}}
\date{February~--~June 2017}

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\begin{document}
\maketitle

\begin{abstract}
    \todo{enhance abstract?}

    The present report describes and summarizes my 1st year of Master's
    degree's internship at the university of Chalmers, Göteborg, Sweden. I
    worked under supervision of Carl-Johan Seger and Mary Sheeran.

    The project's goal was to contribute to VossII, a hardware proving tool
    cloning fl, developed at Intel by Carl Seger for internal usage only. It
    led me to work on \emph{pattern-matching} on electronic circuits for a
    \emph{search-and-replace} method, allowing one to apply previously proved
    transformations to a circuit.

    This problem turns out to be more or less the \emph{subgraph isomorphism
    problem}, which is NP-complete, and must nevertheless be solved fast on
    processor-sized circuits on this particular case.

    During my internship, I developed a C++ library to perform this task that
    will be integrated in VossII, based on a few well-known algorithms as well
    as some ad-hoc heuristics and algorithm tweaks to better match the context
    of circuits. One of my objectives all along was also to keep a clean and
    well-documented codebase, as the tool will have to be maintainable by
    others later.
\end{abstract}

\tableofcontents

\todo{Talk of the repo, somewhere}

\pagebreak

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\section{Introduction}

In the previous years, verification and proved software has gathered an
increasing interest in the computer science community, as people realised how
hard bugs are to track down. But hardware bugs are even more tedious to find
and fix, and can easily lead to disastrous consequences, as those cannot be
patched on existing hardware. For instance, the well-known Pentium
``\textsc{fdiv}'' bug~\cite{pratt1995fdiv} that affected a large number of
Pentium processors lead to wrong results for some floating point divisions.
Intel had to offer to replace all the defective CPUs, leading to an announced
loss of 475 million dollars~\cite{nicely_fdiv}. Even recently, the Skylake and
Kaby Lake hyperthreading bug had to be patched using microcode, loosing
performance and reliability.

To avoid such disasters, the industry nowadays uses a wide range of techniques
to catch bugs as early as possible --- which, hopefully, is before the product's
release date. Among those are \todo{list + cite}, but also proved hardware. On
circuits as complex as processors, usually, only sub-components are proved
correct in a specified context --- that should, but is not proved to, be
respected by the other parts of the circuit. Yet, this trade-off between proved
correctness and engineer's work time already gives a pretty good confidence in
the circuit.

In this context, Carl Seger was one of the main developers of fl at Intel, a
functional ml-inspired programming language integrating many features useful to
get insights of a circuit, testing it and proving it. It mostly features
symbolic trajectory evaluation based model checking and theorem proving, and is
intended to be an all-purpose toolbox for the hardware prover.

Among other features, it includes a ``search and replace'' feature, which can
search every occurrence of a given gates pattern in a circuit, and replace it
by some other gates pattern, proved observationally equivalent beforehand.
Time has proved this method very efficient to design circuits: this way, one
can start from an inefficient, yet simple circuit, prove it, and then refine it
into an equivalent, yet efficient one, through proved transformations. It is
also possible to go the other way, and start with an optimized circuit, hard to
understand, and make it easier to understand to work more efficiently.

\paragraph{My internship} lies amid a project shared between Carl-Johan Seger
and Mary Sheeran, aiming to develop tools for proved design of FPGA circuits.
One of the keystones of this project is an open-sourced and publicly available
version of fl, used for the proving part, and is still at the moment under
development.

My part of the work resided on this ``search and replace'' tool. More
specifically, I focused on writing a C++ library, \emph{isomatch}, which is
interfaced with the C core of fl and provides it with low-level and very fast
functions for this task.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Problem setting}

\subsection{Circuit description}

\begin{figure}[!h]
    \begin{align*}
        \textbf{Integer constant } n, m, p, q, \ldots \qquad& \\
        \\
        \textbf{Wire } in0, out0, ctl0, \ldots \qquad& \\
        \\
        \textbf{Vector } \evec{v}{n} & & \textit{($n$ elements of type $v$)} \\
        \\
        \textbf{Circuit } c, d, \ldots ::=~
            &\text{delay} (in0, out0) &\textit{(delay 1 clock tick)} \\
            \vert~&\text{tristate} (in0, out0, ctl0)
                &\textit{(three-state gate)} \\
            \vert~&\text{comb} (\evec{in0}{n}, \evec{out0}{m}, \evec{e}{m})
                &\textit{(combinatorial gate)} \\
            \vert~&\text{assert} (\evec{in0}{n}, \evec{e}{m})
                &\textit{(assertion gate)} \\
            \vert~&\text{group} (\evec{in0}{n}, \evec{out0}{m}, \evec{c}{p})
                &\textit{(circuit hierarchical group)} \\
        \\
        \textbf{Binary operator } \otimes ::=~
            &\wedge & \textit{(and)} \\
            \vert~&\vee & \textit{(or)} \\
            \vert~&\oplus & \textit{(xor)} \\
            \vert~&+ & \textit{(add)} \\
            \vert~&- & \textit{(sub)} \\
            \vert~&\times & \textit{(times)} \\
            \vert~&\div & \textit{(div)} \\
            \vert~&\% & \textit{(mod)} \\
            \vert~&\lsl & \textit{(logical shift left)} \\
            \vert~&\lsr & \textit{(logical shift right)} \\
            \vert~&\asr & \textit{(arithmetic shift right)} \\
        \\
        \textbf{Unary and constant operator } \otimes_0 ::=~
            &\clsl & \textit{(logical shift left of constant)} \\
            \vert~&\clsr & \textit{(logical shift right of constant)} \\
            \vert~&\casr & \textit{(arithmetic shift right of constant)} \\
        \\
        \textbf{Unary operator } \ominus ::=~
            &\lnot & \textit{(logical negation)} \\
        \\
        \textbf{Expression } e, f, \ldots ::=~
            & x & \textit{(variable)} \\
            \vert~& n & \textit{(integer constant)} \\
            \vert~& e \otimes f & \textit{(binary operator)} \\
            \vert~& e \otimes_0 n & \textit{(unary operator with constant)} \\
            \vert~& \ominus e & \textit{(unary operator)} \\
            \vert~& e_{\vert~n \ldots m} & \textit{(slicing: take a subword)}\\
            \vert~& e~\vert~f & \textit{(merging: concatenate two words)} \\
    \end{align*}
    \caption{AST of circuits used}\label{fig:ast}
\end{figure}

The circuits on which \emph{isomatch} is working are described, and internally
represented, by the AST in Figure~\ref{fig:ast}.

The most important thing in the description of circuits here, is that those
circuits are organized as a hierarchy of \emph{circuit groups}. This hierarchy
can be seen as the construction of a circuit by assembling smaller integrated
circuits (ICs), themselves built the same way, etc. A group is composed of
sub-circuits, input pins and output pins. Each level can of course contain
``leaf'' gates, like \textit{and} or \textit{delay} gates. This is important,
because it allows the program to work on smaller areas the circuit (\eg{}
loading in memory only a part of the circuit, etc.).

\subsection{Objective}

More precisely, the problems that \emph{isomatch} must solve are the following.

\begin{enumerate}
    \item\label{prob:equal} Given two circuit groups, are they structurally
        equivalent? That is, are they the same circuit, arranged in a different
        way, with possibly different names, etc.?

    \item\label{prob:match} Given two circuits, \emph{needle} and
        \emph{haystack}, find every (non-overlapping) occurrence of \emph{needle} in
        \emph{haystack}. An occurrence is a set $S$ of sub-circuits of
        \emph{haystack} such that there is a one-to-one mapping of structurally
        equivalent circuits of $S$ with circuits of \emph{needle}, and those
        circuits are connected the same way in both circuits.
\end{enumerate}

Both problems are hard. The first one is an instance of graph isomorphism, as
the actual question is whether there exists a one-to-one mapping between
sub-circuits of the two groups, such that every mapped circuit is equal to the
other (either directly if it is a leaf gate, or recursively with the same
procedure); and whether this mapping respects connections (edges) between those
circuits. Graph isomorphism is known to be in NP (given a permutation of the
first graph, it is polynomial to check whether the first is equal to the second
\wrt{} the permutation), but not known to be in either P or NP-complete. Thus,
since Babai's work on graph isomorphism~\cite{babai2016graph} is only of
theoretical interest, the known algorithms remain in worst-case exponential
time, and require ad-hoc heuristics for specific kind of graphs to get maximum
efficiency.

The second one is an instance of subgraph isomorphism problem, which is known
to be NP-complete~\cite{cook1971complexity}. Even though a few algorithms
(discussed later) are known to be efficient in most cases for this problem, it
is nevertheless necessary to implement them the right way, and with the right
heuristics, to get the desired efficiency for the given problem.

\subsection{Sought efficiency}

The goal of \textit{isomatch} is to be applied to large circuits on-the-fly,
during their conception. Those circuits can (and will probably) be as large as
a full processor, and the software will be operated by a human, working on
their circuit. Thus, \textit{isomatch} must be as fast as possible, since
matching operation will be executed often, and often multiple times in a row.
It must then remain fast enough for the human not to lose too much time, and
eventually lose patience.

\todo{Mention clean codebase somewhere}

\todo{Mention VossII somewhere}

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\section{General approach}

The global strategy used to solve efficiently the problem can be broken down to
three main parts.

\paragraph{Signatures.} The initial idea to make the computation fast is to
aggregate the inner data of a gate --- be it a leaf gate or a group --- in a
kind of hash, a 64 bits unsigned integer. This approach is directly inspired
from what was done in fl, back at Intel. This hash must be easy to compute,
and must be based only on the structure of the graph --- that is, must be
entirely oblivious of the labels given, the order in which the circuit is
described, the order in which different circuits are plugged on a wire, \ldots.
The signature equality, moreover, must be sound; that is, two signatures must
necessarily be equal if the circuits are indeed equal.

This makes it possible to rule out quickly whether two circuits are candidates
for a match or not, and run the costy actual equality algorithm on fewer gates.


\paragraph{Group equality.} The group equality algorithm is a standard
backtracking algorithm. It tries to build a match between the graphs by
trying the diverse permutations of elements with the same signature. It can
also communicate with the signing part, to request a more precise (but slightly
slower to compute) signature when it has too many permutations to try.

This part could be enhanced, but does not slow down the algorithm on the tested
examples, so I focused on other parts.


\paragraph{Pattern matching.} This part is the one responsible to answer
queries for occurrences of a sub-circuit in a circuit. It uses extensively the
signatures to determine whether two circuits could be a match or not before
spending too much time actually finding matches, but cannot rely on it as
heavily as group equality, since only the first level of precision is
applicable here (detailed later).

This part mostly consists in an implementation of Ullmann's algorithm for
subgraph isomorphism~\cite{ullmann1976algorithm}, a well-known algorithm for
this problem, that uses the specificities of the graph to be a little faster.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Signatures}

The signature is computed as a simple hash of the element, and is defined for
every type of expression and circuit. It could probably be enhanced with a bit
more work to cover more uniformly the hash space, but no collision was observed
on the examples tested.

\paragraph{Signature constants.} Signature constants are used all around the
signing process, and is a 5-tuple $\sigconst{} = (a, x_l, x_h, d_l, d_h)$ of 32
bits unsigned numbers. All of $x_l$, $x_h$, $d_l$ and $d_h$ are picked as prime
numbers between $10^8$ and $10^9$ (which just fits in a 32 bits unsigned
integer); while $a$ is a random integer uniformly picked between $2^{16}$ and
$2^{32}$.  These constants are generated by a small python script,
\path{util/primegen/pickPrimes.py}.

Those constants are used to produce a 64 bits unsigned value out of another 64
bits unsigned value, called $v$ thereafter, through an operator $\sigop$,
computed as follows.

\begin{algorithmic}
    \Function{$\sigop$}{$\sigconst{}, v$}
        \State{} $out1 \gets (v + a) \cdot x_l$
        \State{} $v_h \gets (v \lsr 32) \xor (out1 \lsr 32)$
        \State{} $low \gets out1 \,\%\, d_l$
        \State{} $high \gets \left((v_h + a) \cdot x_h \right) \%\, d_h$
        \State{} \Return{} $low + 2^{32} \cdot high$
    \EndFunction{}
\end{algorithmic}

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\section{Group equality}
\todo{}

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\section{Pattern-match}
\todo{}

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\section{Performance}
\todo{}

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\end{document}