Upgrade of parallelization slides

diff --git a/reservoir.tex b/reservoir.tex
index 594ad47468798fea9fe6739db11006fd85e3dec7..48ecec86301d3ceb54a31f5bb7ea484662404adc 100644 (file)
--- a/reservoir.tex
+++ b/reservoir.tex
@@ -16,7 +16,7 @@
 of the neuromorphic simulation code.
 Application on the MNIST problem} 
 
 of the neuromorphic simulation code.
 Application on the MNIST problem} 
 

-\author{Rapha\"el Couturier, {\bf Michel Salomon}}
+\author{Rapha\"el Couturier, Michel Salomon}

 
 \institute[FEMTO-ST Institute]{\textit{FEMTO-ST - DISC Department - AND Team}}
 
 
 \institute[FEMTO-ST Institute]{\textit{FEMTO-ST - DISC Department - AND Team}}
 


@@ -69,7 +69,7 @@ Application on the MNIST problem}
     \begin{itemize}
     \item Study the concept of Reservoir Computing
     \item Design a faster simulation code
     \begin{itemize}
     \item Study the concept of Reservoir Computing
     \item Design a faster simulation code

-    \item Apply it to a new problem
+    \item Apply it to new problems

     \end{itemize}
   \end{femtoBlock}
 \end{frame}
     \end{itemize}
   \end{femtoBlock}
 \end{frame}


@@ -88,7 +88,7 @@ Application on the MNIST problem}
     \centering
     \includegraphics[width=7.5cm]{ntc.png}
     \begin{itemize}
     \centering
     \includegraphics[width=7.5cm]{ntc.png}
     \begin{itemize}

-    \item $\delta_t \rightarrow \mbox{temporal spacing}; \tau_D \rightarrow \mbox{time delay}$
+    \item $\delta \tau \rightarrow \mbox{temporal spacing}; \tau_D \rightarrow \mbox{time delay}$

     \item $f(x) \rightarrow \mbox{nonlinear transformation}; h(t) \rightarrow \mbox{impulse response}$
     \end{itemize}
   \end{femtoBlock}
     \item $f(x) \rightarrow \mbox{nonlinear transformation}; h(t) \rightarrow \mbox{impulse response}$
     \end{itemize}
   \end{femtoBlock}


@@ -173,77 +173,102 @@ Application on the MNIST problem}
   % Grandes lignes a partir du pdf de Laurent
   \begin{femtoBlock}
     {Main lines\\}
   % Grandes lignes a partir du pdf de Laurent
   \begin{femtoBlock}
     {Main lines\\}

-    \begin{itemize}
-    \item Numerical integration to compute the nonlinear transient
-      response (runge kuta in C)
-    \item[] $\Rightarrow$ computation of matrices $A$ and $B$
-    \item Computation of the Readout
-    \item Test of the solution (cross validation)
-    \end{itemize}
+    \begin{enumerate}
+    \item Pre-processing
+      \begin{itemize}
+      \item Input data formating (1D~vector; sampling period $\rightarrow \delta \tau$)
+      \item $W^I$ initialization (randomly; normalization)
+      \end{itemize}
+    \item Concatenation of 1D~vectors $\rightarrow$ batch processing
+    \item Nonlinear transient computation
+      \begin{itemize}
+      \item Numerical integration using a Runge-Kutta C routine
+      \item Computation of matrices $A$ and $B$
+      \end{itemize}
+    \item Training of the Read-out $\rightarrow$ More-Penrose matrix inversion
+    \item Testing of the solution (cross-validation)
+    \end{enumerate}

   \end{femtoBlock}
   \end{femtoBlock}

+  \smallskip

   % Inconvenient de ce code => temps de calcul
   \begin{femtoBlock}
     {Computation time\\}
   % Inconvenient de ce code => temps de calcul
   \begin{femtoBlock}
     {Computation time\\}

-    12 min for 500 words recognition (2013)
+    \vspace{0.125cm}
+    \centering
+    12 min for 306~``neurons'' on a quad-core i7 1,8~GHz (2013)

   \end{femtoBlock}
 \end{frame}
 
 \section{Parallelization and optimization}
 
 \begin{frame}{Parallelization Scheme} % Slide 8
   \end{femtoBlock}
 \end{frame}
 
 \section{Parallelization and optimization}
 
 \begin{frame}{Parallelization Scheme} % Slide 8

-  \begin{itemize}
-  \item Rewrite the code in C++
-  \item Parallelization with MPI (Message Passing Interface)
-  \item Computation of data response (sound, image) is independent so
-    it can be parallelized (computation of matrices A and B)
-   \item Different regression tests are also independent
-  \item Test of new idea? First test with matlab and then adapt to C++
-    with MPI
-  \end{itemize}
-  % 1 - Comment le paralleliser
-  % 2 - Langage et bibliotheque
-\end{frame}
-
-\begin{frame}{Finding the Optimal Parameters} % Slide 9
-  % 1 - Quels parametres et pourquoi ?

   \begin{femtoBlock}
   \begin{femtoBlock}

-    {What parameters can be optimized?\\}
-    Currently:
+    {Guidelines\\}

     \begin{itemize}
     \begin{itemize}

-    \item Pitch 
-    \item Delta 
-    \item Beta 
-    \item Phi
-    \item Lambda
+    \item Reservoir response is independent, whatever the data \\
+      $\rightarrow$ computation of matrices A and B can be parallelized
+    \item Different regression tests are also independent

     \end{itemize}
     \end{itemize}

-    Next:
+  \end{femtoBlock}
+  \smallskip
+  \begin{femtoBlock}
+    {In practice\\}

     \begin{itemize}
     \begin{itemize}

-    \item Number of nodes that significantly improve the solution (threshold)
-    \item Input filter (convolutional filter for images)
-    \item Potentially any parameters
+    \item Simulation code rewritten in C++
+    \item {\bf M}essage~{\bf P}assing~{\bf I}nterface~for~InterProcess~Communication

     \end{itemize}
     \end{itemize}

-
+  \end{femtoBlock}
+  \smallskip
+  \begin{femtoBlock}
+    {Test of new idea?\\}
+    \vspace{0.25cm}
+    \centering
+    First test with Matlab and then adapt to C++ with MPI

   \end{femtoBlock}
 \end{frame}
 
   \end{femtoBlock}
 \end{frame}
 

-\begin{frame}{Finding the Optimal Parameters} % Slide 10
-  % 2 - Optimisation par recuit simule
+\begin{frame}{Finding Optimal Parameters} % Slide 9
+  % 1 - Quels parametres et pourquoi ?

   \begin{femtoBlock}
   \begin{femtoBlock}

-    {Optimization heuristics\\}
+    {What parameters can be optimized?\\}

     \begin{itemize}
     \begin{itemize}

-    \item Now: Simulated annealing
-    \item[] $\Rightarrow$ probabilistic technique for approximating the global optimum of a given function.
-    \item Next: maybe other heuristics
+    \item Currently
+      \begin{itemize}
+      \item Pitch of the Read-Out
+      \item Amplitude parameters $\rightarrow \delta; \beta; \phi_0$
+      \item Regression parameter $\rightarrow \lambda$
+      \end{itemize}
+    \item Next
+      \begin{itemize}
+      \item Number of nodes significantly improving the solution (threshold)
+      \item Input data filter (convolutional filter for images)
+      \end{itemize}

     \end{itemize}
     \end{itemize}

+    \centering
+    Potentially any parameter can be optimized

   \end{femtoBlock}
   \end{femtoBlock}

+  \smallskip

   \begin{femtoBlock}
   \begin{femtoBlock}

-    {Similar results with the SDR problem\\}
+    {Optimization heuristics\\}
+    \begin{itemize}
+    \item Currently $\rightarrow$ simulated annealing \\
+      {\small (probabilistic global search controlled by a cooling schedule)}
+    \item Next $\rightarrow$ other metaheuristics like evolutionay algorithms
+    \end{itemize}

   \end{femtoBlock}
 \end{frame}
 
   \end{femtoBlock}
 \end{frame}
 

-\begin{frame}{Performances} % Slide 11
-  % 1 - Taux d'erreur en terme de classification
-  % 2 - Gain en temps d'execution / speedup curve
+\begin{frame}{Performances of the parallel code} % Slide 10
+  \begin{femtoBlock}
+    {Speech recognition problem\\}
+    \begin{itemize}
+    \item Same WER $\rightarrow$ similar classification accuracy
+    \item Reduced computation time $\rightarrow$ speedup...
+    \end{itemize}
+    \centering
+    \vspace{0.25cm}
+    We can study problems with huge Matlab computation time
+  \end{femtoBlock}

 \end{frame}
 
 \section{Performances on the MNIST problem}
 \end{frame}
 
 \section{Performances on the MNIST problem}