i8ème commit

[dmems12.git] / dmems12.tex

diff --git a/dmems12.tex b/dmems12.tex
index 3fd1ca1636cf186c473dce04b75c496ee6f81b0d..314368e8ff8e9cad225c946751adb95aa857a125 100644 (file)
--- a/dmems12.tex
+++ b/dmems12.tex
@@ -1,5 +1,5 @@
 
 

-\documentclass[10pt, conference, compsocconf]{IEEEtran}
+\documentclass[10pt, peerreview, compsocconf]{IEEEtran}

 %\usepackage{latex8}
 %\usepackage{times}
 \usepackage[utf8]{inputenc}
 %\usepackage{latex8}
 %\usepackage{times}
 \usepackage[utf8]{inputenc}


@@ -58,7 +58,7 @@
 
 
 
 
 
 

-\maketitle
+%\maketitle

 
 \thispagestyle{empty}
 
 
 \thispagestyle{empty}
 


@@ -66,9 +66,16 @@
 
   
 
 
   
 

-{\it keywords}: FPGA, cantilever, interferometry.
+

 \end{abstract}
 
 \end{abstract}
 

+\begin{IEEEkeywords}
+FPGA, cantilever, interferometry.
+\end{IEEEkeywords}
+
+
+\IEEEpeerreviewmaketitle
+

 \section{Introduction}
 
 Cantilevers  are  used  inside  atomic  force  microscope (AFM) which  provides  high
 \section{Introduction}
 
 Cantilevers  are  used  inside  atomic  force  microscope (AFM) which  provides  high


@@ -226,24 +233,28 @@ In fact, this timing is a very hard constraint. Let consider a very
 small programm that initializes twenty million of doubles in memory
 and then does 1000000 cumulated sums on 20 contiguous values
 (experimental profiles have about this size). On an intel Core 2 Duo
 small programm that initializes twenty million of doubles in memory
 and then does 1000000 cumulated sums on 20 contiguous values
 (experimental profiles have about this size). On an intel Core 2 Duo

-E6650 at 2.33GHz, this program reaches an average of 155Mflops. It
-implies that the phase computation algorithm should not take more than
-$155\times 12.5 = 1937$ floating operations. For integers, it gives
-$3000$ operations. Obviously, some cache effects and optimizations on
+E6650 at 2.33GHz, this program reaches an average of 155Mflops. 
+
+%%Itimplies that the phase computation algorithm should not take more than
+%%$155\times 12.5 = 1937$ floating operations. For integers, it gives $3000$ operations. 
+
+Obviously, some cache effects and optimizations on

 huge amount of computations can drastically increase these
 performances : peak efficiency is about 2.5Gflops for the considered
 CPU. But this is not the case for phase computation that used only few
 tenth of values.\\
 
 In order to evaluate the original algorithm, we translated it in C
 huge amount of computations can drastically increase these
 performances : peak efficiency is about 2.5Gflops for the considered
 CPU. But this is not the case for phase computation that used only few
 tenth of values.\\
 
 In order to evaluate the original algorithm, we translated it in C

-language. Profiles are read from a 1Mo file, as if it was an image
-stored in a device file representing the camera. The file contains 100
-profiles of 21 pixels, equally scattered in the file. We obtained an
-average of 10.5$\mu$s by profile (including I/O accesses). It is under
-are requirements but close to the limit. In case of an occasional load
-of the system, it could be largely overtaken. A solution would be to
-use a real-time operating system but another one to search for a more
-efficient algorithm.
+language. As said further, for 20 pixels, it does about 1550
+operations, thus an estimated execution time of $1550/155
+=$10$\mu$s. For a more realistic evaluation, we constructed a file of
+1Mo containing 200 profiles of 20 pixels, equally scattered. This file
+is equivalent to an image stored in a device file representing the
+camera. We obtained an average of 10.5$\mu$s by profile (including I/O
+accesses). It is under are requirements but close to the limit. In
+case of an occasional load of the system, it could be largely
+overtaken. A solution would be to use a real-time operating system but
+another one to search for a more efficient algorithm.

 
 But the main drawback is the latency of such a solution : since each
 profile must be treated one after another, the deflection of 100
 
 But the main drawback is the latency of such a solution : since each
 profile must be treated one after another, the deflection of 100


@@ -269,41 +280,47 @@ some hardware constraints specific to FPGAs.
 \section{Proposed solution}
 \label{sec:solus}
 
 \section{Proposed solution}
 \label{sec:solus}
 

-Project Oscar aims to provide an hardware and software architecture to
-estimate and control the deflection of cantilevers. The hardware part
-consists in a high-speed camera, linked on an embedded board hosting
-FPGAs. By the way, the camera output stream can be pushed directly
-into the FPGA. The software part is mostly the VHDL code that
-deserializes the camera stream, extracts profile and computes the
-deflection. Before focusing on our work to implement the phase
-computation, we give some general informations about FPGAs and the
-board we use.
+Project Oscar aims  to provide a hardware and  software architecture to estimate
+and  control the  deflection of  cantilevers. The  hardware part  consists  in a
+high-speed camera,  linked on an embedded  board hosting FPGAs. By  the way, the
+camera output stream can be pushed  directly into the FPGA. The software part is
+mostly the VHDL  code that deserializes the camera  stream, extracts profile and
+computes  the deflection. Before  focusing on  our work  to implement  the phase
+computation, we give some general information about FPGAs and the board we use.

 
 \subsection{FPGAs}
 
 
 \subsection{FPGAs}
 

-A field-programmable gate  array (FPGA) is an integrated  circuit designed to be
-configured by  the customer.  A hardware  description language (HDL)  is used to
-configure a  FPGA. FGPAs are  composed of programmable logic  components, called
-logic blocks.  These blocks can be  configured to perform simple (AND, XOR, ...)
-or  complex  combinational  functions.    Logic  blocks  are  interconnected  by
-reconfigurable  links. Modern  FPGAs  contains memory  elements and  multipliers
-which enables to simplify the design and increase the speed. As the most complex
-operation operation on FGPAs is the  multiplier, design of FGPAs should not used
-complex operations. For example, a divider  is not an available operation and it
-should be programmed using simple components.
-
+A field-programmable gate array (FPGA) is an integrated circuit
+designed to be configured by the customer. FGPAs are composed of
+programmable logic components, called configurable logic blocks
+(CLB). These blocks mainly contains look-up tables (LUT), flip/flops
+(F/F) and latches, organized in one or more slices connected
+together. Each CLB can be configured to perform simple (AND, XOR, ...)
+or complex combinational functions. They are interconnected by
+reconfigurable links. Modern FPGAs contain memory elements and
+multipliers which enable to simplify the design and to increase the
+performance. Nevertheless, all other complex operations, like
+division, trigonometric functions, $\ldots$ are not available and must
+be done by configuring a set of CLBs.
+
+Since this configuration is not obvious at all, it can be done via a
+framework that synthetize a design written in an hardware description
+language (HDL), and after, that place and route 
+
+ is used to configure a FPGA.

 FGPAs programming  is very different  from classic processors  programming. When
 FGPAs programming  is very different  from classic processors  programming. When

-logic block are programmed and linked  to performed an operation, they cannot be
-reused anymore.  FPGA  are cadenced more slowly than classic  processors but they can
-performed pipelined as  well as parallel operations. A  pipeline provides a way
-manipulate data quickly  since at each clock top to handle  a new data. However,
-using  a  pipeline  consomes more  logics  and  components  since they  are  not
-reusable,  nevertheless it  is probably  the most  efficient technique  on FPGA.
-Parallel  operations   can  be  used   in  order  to  manipulate   several  data
+logic blocks are  programmed and linked to perform an  operation, they cannot be
+reused anymore.  FPGAs are cadenced more slowly than classic processors but they
+can perform pipeline  as well as parallel operations. A  pipeline provides a way
+to  manipulate  data  quickly  since  at   each  clock  top  it  handles  a  new
+data. However, using  a pipeline consumes more logics  and components since they
+are not  reusable. Nevertheless it is  probably the most  efficient technique on
+FPGA.   Parallel operations  can be  used in  order to  manipulate  several data

 simultaneously. When  it is  possible, using  a pipeline is  a good  solution to
 manipulate  new  data  at  each  clock  top  and  using  parallelism  to  handle
 simultaneously. When  it is  possible, using  a pipeline is  a good  solution to
 manipulate  new  data  at  each  clock  top  and  using  parallelism  to  handle

-simultaneously several data streams.
+simultaneously several pipelines in order to handle multiple data streams.

 
 

+%% parler du VHDL, synthèse et bitstream

 \subsection{The board}
 
 The board we use is designed by the Armadeus compagny, under the name
 \subsection{The board}
 
 The board we use is designed by the Armadeus compagny, under the name


@@ -587,7 +604,7 @@ largely beyond the worst experimental ones.
 
 \begin{figure}[ht]
 \begin{center}
 
 \begin{figure}[ht]
 \begin{center}

-  \includegraphics[width=9cm]{intens-noise20-spl}
+  \includegraphics[width=9cm]{intens-noise20}

 \end{center}
 \caption{Sample of worst profile for N=10}
 \label{fig:noise20}
 \end{center}
 \caption{Sample of worst profile for N=10}
 \label{fig:noise20}


@@ -595,7 +612,7 @@ largely beyond the worst experimental ones.
 
 \begin{figure}[ht]
 \begin{center}
 
 \begin{figure}[ht]
 \begin{center}

-  \includegraphics[width=9cm]{intens-noise60-lsq}
+  \includegraphics[width=9cm]{intens-noise60}

 \end{center}
 \caption{Sample of worst profile for N=30}
 \label{fig:noise60}
 \end{center}
 \caption{Sample of worst profile for N=30}
 \label{fig:noise60}


@@ -608,7 +625,7 @@ SPL on $N = k\times M$, i.e. the number of interpolated points.
 
 We assume that $M=20$, $nb_s=1024$, $k=4$, all possible parts are
 already in lookup tables and a limited set of operations (+, -, *, /,
 
 We assume that $M=20$, $nb_s=1024$, $k=4$, all possible parts are
 already in lookup tables and a limited set of operations (+, -, *, /,

-<, >) is taken account. Translating the two algorithms in C code, we
+$<$, $>$) is taken account. Translating the two algorithms in C code, we

 obtain about 430 operations for LSQ and 1550 (plus few tenth for
 $atan$) for SPL. This result is largely in favor of LSQ. Nevertheless,
 considering the total number of operations is not really pertinent for
 obtain about 430 operations for LSQ and 1550 (plus few tenth for
 $atan$) for SPL. This result is largely in favor of LSQ. Nevertheless,
 considering the total number of operations is not really pertinent for


@@ -653,15 +670,30 @@ mapping and routing the design on the Spartan6. By the way,
 extra-latency is generated and there must be idle times between two
 profiles entering into the pipeline.
 
 extra-latency is generated and there must be idle times between two
 profiles entering into the pipeline.
 

-Before obtaining the least bitstream, the crucial question is : how to
-translate the C code the LSQ into VHDL ?
+%%Before obtaining the least bitstream, the crucial question is : how to
+%%translate the C code the LSQ into VHDL ?
+

 
 

+%\subsection{VHDL design paradigms}

 
 

-\subsection{VHDL design paradigms}
+\section{Experimental tests}

 
 \subsection{VHDL implementation}
 
 
 \subsection{VHDL implementation}
 

-\section{Experimental results}
+% - ecriture d'un code en C avec integer
+% - calcul de la taille max en bit de chaque variable en fonction de la quantization.
+% - tests de quantization : équilibre entre précision et contraintes FPGA
+% - en parallèle : simulink et VHDL à la main
+%
+\subsection{Simulation}
+
+% ghdl + gtkwave
+% au mieux : une phase tous les 33 cycles, latence de 95 cycles.
+% mais routage/placement impossible.
+\subsection{Bitstream creation}
+
+% pas fait mais prévision d'une sortie tous les 480ns avec une latence de 1120
+

 \label{sec:results}
 
 
 \label{sec:results}