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| -rw-r--r-- | Presentation/presentation.tex | 102 | ||||
| -rw-r--r-- | 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 |