From 9a36663414b96f30652ba5503753f7c16a7dcaa6 Mon Sep 17 00:00:00 2001
From: Leonard Kugis <leonard@kug.is>
Date: Wed, 11 Jan 2023 23:52:17 +0100
Subject: Deleted backup slides

---
 Presentation/presentation.tex | 102 +++++++++++++++++++++---------------------
 Presentation/transcript.md    |   4 +-
 2 files changed, 53 insertions(+), 53 deletions(-)

diff --git a/Presentation/presentation.tex b/Presentation/presentation.tex
index 6f6b970..4cdd193 100644
--- a/Presentation/presentation.tex
+++ b/Presentation/presentation.tex
@@ -66,17 +66,17 @@ backend=biber,
   \begin{figure}[h]
     \centering
     \includegraphics[width=\textwidth, keepaspectratio]{resources/cnn}
-    \caption{Deep Neural Network \cite{726791}}
+    \caption{Deep Neural Network (LeNet) \cite{726791}}
   \end{figure}
 \end{frame}
 
-\begin{frame}{Deep Neural Networks}
-  \begin{figure}[h]
-    \centering
-    \includegraphics[width=\textwidth, keepaspectratio]{resources/fcn}
-    \caption{Fully connected layer}
-  \end{figure}
-\end{frame}
+% \begin{frame}{Deep Neural Networks}
+%   \begin{figure}[h]
+%     \centering
+%     \includegraphics[width=\textwidth, keepaspectratio]{resources/fcn}
+%     \caption{Fully connected layer}
+%   \end{figure}
+% \end{frame}
 
 \begin{frame}{Deep Neural Networks}
   \begin{figure}[h]
@@ -140,20 +140,20 @@ backend=biber,
   \tableofcontents[currentsection]
 \end{frame}
 
-\begin{frame}{Data types}
-  \begin{itemize}
-    \item Dynamic
-    \begin{itemize}
-      \item Input data
-      \item Output data
-    \end{itemize}
-    \item Static (parameters)
-    \begin{itemize}
-      \item Weights
-      \item Parameters of activation functions
-    \end{itemize}
-  \end{itemize}
-\end{frame}
+% \begin{frame}{Data types}
+%   \begin{itemize}
+%     \item Dynamic
+%     \begin{itemize}
+%       \item Input data
+%       \item Output data
+%     \end{itemize}
+%     \item Static (parameters)
+%     \begin{itemize}
+%       \item Weights
+%       \item Parameters of activation functions
+%     \end{itemize}
+%   \end{itemize}
+% \end{frame}
 
 \begin{frame}{AlexNet}
   \begin{itemize}
@@ -240,14 +240,6 @@ backend=biber,
   \end{itemize}
 \end{frame}
 
-\begin{frame}{Weight quantization}
-  \begin{figure}[h]
-    \centering
-    \includegraphics[width=0.8\textwidth, keepaspectratio]{resources/centroid_initialization}
-    \caption{Different centroid initialization methods \cite{Han2015DeepCC}}
-  \end{figure}
-\end{frame}
-
 \begin{frame}{Huffman encoding}
   \begin{figure}[h]
     \centering
@@ -264,25 +256,6 @@ backend=biber,
   \end{figure}
 \end{frame}
 
-\begin{frame}{HashNets}
-  \begin{minipage}{0.49\linewidth}
-    \begin{figure}[h]
-      \centering
-      \includegraphics[width=\textwidth, keepaspectratio]{resources/hashnets}
-    \end{figure}
-  \end{minipage}
-  \hfill
-  \begin{minipage}{0.49\linewidth}
-    \begin{itemize}
-      \item Virtual weight matrix $\textbf{V}^{\ell}$
-      \item One-way hash function $h^{\ell}(i, j)$
-      \item Weight array $w^{\ell}$
-      \item Hash function returns index for weight array
-      \item $w^{\ell}_{h^{\ell}(i, j)} = \textbf{V}^{\ell}_{ij}$
-    \end{itemize}
-  \end{minipage}
-\end{frame}
-
 \begin{frame}{Storage format}
   \begin{itemize}
     \item Compressed sparse column (CSC) /
@@ -400,9 +373,9 @@ backend=biber,
   \hfill
   \begin{minipage}{0.59\linewidth}
     \begin{itemize}
-      \item Receives column vector $v$, absolute destination accumulator register index $x$ and activation value $a_j$
+      \item Receives column vector $v$, relative index $z$ and activation value $a_j$
+      \item Calculates absolute destination accumulator register index $x$
       \item Calculates $b_x = b_x + v \cdot a_j$
-      \item Accumulates indices $x$ and forwards real target address 
     \end{itemize}
   \end{minipage}
 \end{frame}
@@ -503,4 +476,31 @@ backend=biber,
   End
 \end{frame}
 
+\begin{frame}{Weight quantization}
+  \begin{figure}[h]
+    \centering
+    \includegraphics[width=0.8\textwidth, keepaspectratio]{resources/centroid_initialization}
+    \caption{Different centroid initialization methods \cite{Han2015DeepCC}}
+  \end{figure}
+\end{frame}
+
+\begin{frame}{HashNets}
+  \begin{minipage}{0.49\linewidth}
+    \begin{figure}[h]
+      \centering
+      \includegraphics[width=\textwidth, keepaspectratio]{resources/hashnets}
+    \end{figure}
+  \end{minipage}
+  \hfill
+  \begin{minipage}{0.49\linewidth}
+    \begin{itemize}
+      \item Virtual weight matrix $\textbf{V}^{\ell}$
+      \item One-way hash function $h^{\ell}(i, j)$
+      \item Weight array $w^{\ell}$
+      \item Hash function returns index for weight array
+      \item $w^{\ell}_{h^{\ell}(i, j)} = \textbf{V}^{\ell}_{ij}$
+    \end{itemize}
+  \end{minipage}
+\end{frame}
+
 \end{document}
\ No newline at end of file
diff --git a/Presentation/transcript.md b/Presentation/transcript.md
index 513656b..b2ba006 100644
--- a/Presentation/transcript.md
+++ b/Presentation/transcript.md
@@ -72,11 +72,11 @@
 
 ## Komprimierung
 
-- Welche verschiedenen Speicherarten haben wir in einem Accelerator?
+<!-- - Welche verschiedenen Speicherarten haben wir in einem Accelerator?
 - Dynamisch: Eingabedaten
 - Statisch: Gewichte, Parameter für Aktivierungsfunktionen
 
-*nächste Folie*
+*nächste Folie* -->
 
 ## AlexNet
 
-- 
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