X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/presentation_reservoir.git/blobdiff_plain/c420ac31a3a51a6f1a318ba4e979fe02d7dcdb6d..0f62d4cc7837ade5e2b5f34a3d09a910bd211511:/reservoir.tex?ds=inline

diff --git a/reservoir.tex b/reservoir.tex
index d3ca368..48ecec8 100644
--- a/reservoir.tex
+++ b/reservoir.tex
@@ -16,7 +16,7 @@
 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}}
 
@@ -69,7 +69,7 @@ Application on the MNIST problem}
     \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}
@@ -88,7 +88,7 @@ Application on the MNIST problem}
     \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}
@@ -173,31 +173,102 @@ Application on the MNIST problem}
   % Grandes lignes a partir du pdf de Laurent
   \begin{femtoBlock}
     {Main lines\\}
+    \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}
+  \smallskip
   % Inconvenient de ce code => temps de calcul
   \begin{femtoBlock}
-   {Computation time\\}
+    {Computation time\\}
+    \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
-  % 1 - Comment le paralleliser
-  % 2 - Langage et bibliotheque
+  \begin{femtoBlock}
+    {Guidelines\\}
+    \begin{itemize}
+    \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{femtoBlock}
+  \smallskip
+  \begin{femtoBlock}
+    {In practice\\}
+    \begin{itemize}
+    \item Simulation code rewritten in C++
+    \item {\bf M}essage~{\bf P}assing~{\bf I}nterface~for~InterProcess~Communication
+    \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}
 
-\begin{frame}{Finding the Optimal Parameters} % Slide 9
+\begin{frame}{Finding Optimal Parameters} % Slide 9
   % 1 - Quels parametres et pourquoi ?
+  \begin{femtoBlock}
+    {What parameters can be optimized?\\}
+    \begin{itemize}
+    \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}
+    \centering
+    Potentially any parameter can be optimized
+  \end{femtoBlock}
+  \smallskip
+  \begin{femtoBlock}
+    {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}
 
-\begin{frame}{Finding the Optimal Parameters} % Slide 10
-  % 2 - Optimisation par recuit simule
-\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}
@@ -220,6 +291,15 @@ Application on the MNIST problem}
 
 \begin{frame}{Conclusion and perspectives} % Slide 15
 
+
+  Many perspectives (we are just beginning)
+  Improvement of the code\\
+  Test of many ideas : number of comities\\
+  One reservoir to learn and another one to learn error and correct
+  them\\
+  Test other large problems in simulation before in real\\
+  => Try to test many configuration and to find optimal parameters
+  
 \end{frame}
 
 \begin{frame}{Thank you for your attention}