Do some fine tuning

main.tex

@@ -17,6 +17,7 @@
 \addbibresource{references.bib}
 
 \setbeamerfont{footnote}{size=\tiny}
+\setbeamercolor{alerted text}{fg=uniwuered}
 
 \newdate{presentationday}{01}{10}{2024}
 
@@ -52,47 +53,24 @@
 
 % \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}
-
-\note[itemize]{
-    \item compute 2x every two years
-    \item energy production 2\% per year
-    \item to meet future compute demands, drastic improvements in energy efficiency
-}
-
-\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}
-
-\note[itemize]{
-    \item Emerging AI applications become increasingly memory-bound
-    \item Roofline model
-    \item Not limited by compute power but by memory
-    \item researchers begin to consider PIM to circumvent memory bottleneck
-    \item (drastically more parameters in GPT-3, operational intensity goes down)
-}
-
 % \section{Processing-in-Memory}
 
 \begin{frame}{Workloads for PIM}
     Fully connected neural network layers:
     \begin{itemize}
         \item Large weight matrix - \alert{does not fit onto cache}
-        \item No data reuse - \alert{cache is useless}
+        \item No data reuse - \alert{cache is almost useless}
     \end{itemize}
     \begin{figure}
         \includesvg[width=0.6\textwidth]{images/dnn}
     \end{figure}
 \end{frame}
 
+\note[itemize]{
+    \item Let's start by having a look on which workloads can be accelerated by PIM...
+    \item memory-bound: each entry only used once
+}
+
 \begin{frame}{Workloads for PIM}
     Convolutional layers:
     \begin{itemize}
@@ -106,10 +84,17 @@
     \end{figure}
 \end{frame}
 
+\note[itemize]{
+    \item compute-bound: elements of filter matrix used often
+}
+
 \begin{frame}{Workloads for PIM}
     \begin{columns}[T]
         \begin{column}{0.5\textwidth}
-            \begin{center} \includesvg[height=50px]{images/thumbs-up} \end{center}
+            \begin{center}
+                \includesvg[height=50px]{images/thumbs-up}
+            \end{center}
+            \begin{center}\alert{(memory-bound)}\end{center}
             \begin{itemize}
                 \item Fully connected layers in multilayer perceptrons (MLPs)
                 \item Layers in recurrent neural networks (RNNs)
@@ -117,6 +102,7 @@
         \end{column}
         \begin{column}{0.5\textwidth}
             \begin{center} \includesvg[height=50px]{images/thumbs-unsure} \end{center}
+            \begin{center}\alert{(compute-bound)}\end{center}
             \begin{itemize}
                 \item Convolutional neural network (CNNs)
             \end{itemize}
@@ -133,7 +119,7 @@
         \begin{column}{0.4\textwidth}
             \begin{itemize}
                 \item<2-> Inside the memory subarray
-                \item<3-> Near the subarray in the PSA output region
+                \item<3-> Near the subarray in the PSA region
                 \item<4-> Near the bank in its peripheral region
                 \item<5-> In the I/O region of the memory
             \end{itemize}
@@ -152,7 +138,7 @@
 \note[itemize]{
     \item Architecture space of PIM:
     \item Inside the memory SA - simple bulk logic
-    \item Near SA in PSA output region - logic gates in the region
+    \item Near SA in PSA region - logic gates in the region
     \item Near a bank in its peripheral region - computation units with control
     \item I/O region of memory - limited by memory bus
 }
@@ -162,7 +148,7 @@
 \begin{frame}{Samsung HBM-PIM/FIMDRAM\autocite{lee2021}}
     \begin{itemize}
         \item Real-world PIM implementation based on HBM2
-        \item PIM units embedded at the bank level
+        \item PIM units embedded at the bank level - in parallel
     \end{itemize}
     \begin{figure}
         \includesvg[width=0.8\textwidth]{images/hbm-pim}
@@ -314,9 +300,11 @@
         \begin{column}{0.5\textwidth}
             \begin{itemize}
                 \item Vector benchmarks (BLAS level 1)
+                \begin{itemize}
                     \item VADD: $z = x + y$
                     \item VMUL: $z = x \cdot y$
                     \item HAXPY: $z = a \cdot x + y$
+                \end{itemize}
 
                 \item Vector-Matrix benchmarks (BLAS level 2)
                       \begin{itemize}
@@ -397,7 +385,7 @@
     \item GEMV: 9.0x
 }
 
-\begin{frame}{Speedups / Samsung}
+\begin{frame}{Speedups / Samsung\autocite{lee2021}}
     \begin{figure}
         \includesvg[width=0.8\textwidth]{images/samsung.svg}
     \end{figure}
@@ -429,12 +417,13 @@
 \end{frame}
 
 \begin{frame}{Conclusion and Future Work}
+    \textbf{Conclusion}
     \begin{itemize}
         \item PIM can accelerate memory-bound workloads
         \item Special PIM-friendly memory layouts are required
     \end{itemize}
 
-    Future work:
+    \textbf{Future work}
     \begin{itemize}
         \item Implementation of Linux driver
         \item Comparison with real neural networks
@@ -452,6 +441,34 @@
 
 \appendix
 
+\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}
+
+\note[itemize]{
+    \item compute 2x every two years
+    \item energy production 2\% per year
+    \item to meet future compute demands, drastic improvements in energy efficiency
+}
+
+\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}
+
+\note[itemize]{
+    \item Emerging AI applications become increasingly memory-bound
+    \item Roofline model
+    \item Not limited by compute power but by memory
+    \item researchers begin to consider PIM to circumvent memory bottleneck
+    \item (drastically more parameters in GPT-3, operational intensity goes down)
+}
+
 \begin{frame}{HBM-PIM/FIMDRAM GEMV Operation}
     \begin{figure}
         \only<1>{\includesvg[width=0.8\textwidth]{images/gemv_normal}}