Initial commit

main.tex

@@ -0,0 +1,131 @@
+\documentclass[aspectratio=169]{beamer}
+\usetheme{UniWue}
+
+\usepackage[style=verbose-ibid]{biblatex}
+\usepackage{datetime}
+\usepackage[inkscapeversion=1]{svg}
+
+\addbibresource{doc.bib}
+
+\setbeamerfont{footnote}{size=\tiny}
+
+\newdate{presentationday}{01}{10}{2024}
+
+\title{PIMSys}
+\subtitle{A Virtual Prototype for Processing in Memory}
+
+\author{
+    Derek~Christ\inst{1}
+    \and
+    Lukas~Steiner\inst{2}
+    \and
+    Matthias~Jung\inst{3}
+    \and
+    Norbert~Wehn\inst{2}
+}
+
+\institute{
+    \inst{1}
+    Fraunhofer IESE
+    \quad
+    \inst{2}
+    RPTU Kaiserslautern-Landau
+    \quad
+    \inst{3}
+    University of Würzburg
+}
+
+\date{MEMSYS~2024}
+
+\begin{document}
+
+\frame{\titlepage}
+
+\section{Introduction}
+
+\begin{frame}{Energy Demand of Applications}
+    Total compute energy approaches world’s energy production\autocite{src2021}
+    \begin{figure}
+        \includesvg[width=0.6\textwidth]{images/world_energy}
+    \end{figure}
+\end{frame}
+
+\begin{frame}{Memory Bound Workloads}
+    AI applications become increasingly memory-bound\autocite{ivobolsens2023}
+    \begin{figure}
+        \includesvg[width=0.5\textwidth]{images/gpt}
+    \end{figure}
+\end{frame}
+
+\section{Processing-in-Memory}
+
+\begin{frame}{Workloads for PIM}
+    Fully connected neural network layers:
+    \begin{itemize}
+        \item Large weight matrix - does not fit onto cache
+        \item No data reuse - cache is useless
+    \end{itemize}
+    \begin{figure}
+        \includesvg[width=0.6\textwidth]{images/dnn}
+    \end{figure}
+\end{frame}
+
+\begin{frame}{Workloads for PIM}
+    Convolutional layers:
+    \begin{itemize}
+        \item Small filter matrix - does fit onto cache
+        \item Excessive data reuse - cache is useful
+    \end{itemize}
+    \begin{figure}
+        TODO Tikz Image
+        % \includesvg[width=0.6\textwidth]{images/dnn}
+    \end{figure}
+\end{frame}
+
+\begin{frame}{Workloads for PIM}
+    \begin{columns}[T]
+        \begin{column}{0.5\textwidth}
+            \begin{center} \includesvg[height=50px]{images/thumbs-up} \end{center}
+            \begin{itemize}
+                \item Fully connected layers in multilayer perceptrons (MLPs)
+                \item Layers in recurrent neural networks (RNNs)
+            \end{itemize}
+        \end{column}
+        \begin{column}{0.5\textwidth}
+            \begin{center} \includesvg[height=50px]{images/thumbs-unsure} \end{center}
+            \begin{itemize}
+                \item Convolutional neural network (CNNs)
+            \end{itemize}
+        \end{column}
+    \end{columns}
+\end{frame}
+
+\begin{frame}{PIM Architectures}
+    \begin{columns}[T]
+        \begin{column}{0.4\textwidth}
+            \begin{itemize}
+                \item<2-> Inside the memory subarray
+                \item<3-> Near the subarray in the PSA output region
+                \item<4-> Near the bank in its peripheral region
+                \item<5-> In the I/O region of the memory
+            \end{itemize}
+        \end{column}
+        \begin{column}{0.6\textwidth}
+            \only<1>{\includesvg[width=\textwidth]{images/pim_positions_0}}
+            \only<2>{\includesvg[width=\textwidth]{images/pim_positions_1}}
+            \only<3>{\includesvg[width=\textwidth]{images/pim_positions_2}}
+            \only<4>{\includesvg[width=\textwidth]{images/pim_positions_3}}
+            \only<5>{\includesvg[width=\textwidth]{images/pim_positions_4}}
+        \end{column}
+    \end{columns}
+\end{frame}
+
+\begin{frame}{Samsung's HBM-PIM/FIMDRAM}
+\end{frame}
+
+\begin{frame}
+    \frametitle{Outline}
+    \tableofcontents
+\end{frame}
+
+\end{document}