diff --git a/jupyter/performance.ipynb b/jupyter/performance.ipynb
index 29a00d5b361307622a5f211710e3671d47e151e6..c26a5cbad477d85dcb9ac467dd7b2a2781fab322 100644
--- a/jupyter/performance.ipynb
+++ b/jupyter/performance.ipynb
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = models.create_model(pretrained=False, dataset='imagenet', arch='resnet18', parallel=False)"
+    "model = models.create_model(pretrained=False, dataset='imagenet', arch='resnet50', parallel=False)"
    ]
   },
   {
@@ -56,6 +56,49 @@
     "print(\"Total MACs: \" + \"{:,}\".format(total_macs))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's take a look at how our compute is distibuted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"MAC distribution:\")\n",
+    "counts = df['MACs'].value_counts()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's look at which convolutions kernel sizes we're using, and how many instances:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Convolution kernel size distribution:\")\n",
+    "counts = df['Attrs'].value_counts()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's look at how the MACs are distributed between the layers and the convolution kernel sizes"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -64,11 +107,30 @@
    },
    "outputs": [],
    "source": [
+    "def get_layer_color(layer_type, attrs):\n",
+    "    if layer_type == \"Conv2d\":\n",
+    "        if attrs == 'k=(1, 1)':\n",
+    "            return 'tomato'\n",
+    "        elif attrs == 'k=(3, 3)':\n",
+    "            return 'limegreen'\n",
+    "        else:\n",
+    "            return 'steelblue'\n",
+    "    return 'indigo'\n",
+    "\n",
     "df_compute = df['MACs']\n",
-    "ax = df_compute.plot.bar(figsize=[15,10], title=\"MACs\");\n",
+    "ax = df_compute.plot.bar(figsize=[15,10], title=\"MACs\", \n",
+    "                         color=[get_layer_color(layer_type, attrs) for layer_type,attrs in zip(df['Type'], df['Attrs'])])\n",
+    "\n",
     "ax.set_xticklabels(df.Name, rotation=90);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How do the Weights and Feature-maps footprints distribute across the layers:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -81,6 +143,27 @@
     "ax.set_xticklabels(df.Name, rotation=90);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How the Arithmetic Intensity distributes across the layers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_performance = df\n",
+    "df_performance['raw traffic'] = df_footprint['FM volume'] + df_footprint['Weights volume']\n",
+    "df_performance['arithmetic intensity'] = df['MACs'] / df_performance['raw traffic']\n",
+    "df_performance2 = df_performance['arithmetic intensity']\n",
+    "ax = df_performance2.plot.bar(figsize=[15,10], title=\"Arithmetic Intensity\");\n",
+    "ax.set_xticklabels(df.Name, rotation=90);"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},