{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Before You Start\n",
    "\n",
    "The current set of notebooks are under constant development.\n",
    "\n",
    "## Update Tutorial Repository\n",
    "\n",
    "If you have previously cloned the tutorial repository, you may need to get the latest versions of the notebooks.\n",
    "\n",
    "First check the status of your repository:\n",
    "```\n",
    "cd hls4ml-tutorial\n",
    "make clean\n",
    "git status \n",
    "```\n",
    "\n",
    "You may have some _modified_ notebooks. For example:\n",
    "\n",
    "```\n",
    "# On branch csee-e6868-spring2021\n",
    "# Changes not staged for commit:\n",
    "#   (use \"git add <file>...\" to update what will be committed)\n",
    "#   (use \"git checkout -- <file>...\" to discard changes in working directory)\n",
    "#\n",
    "#\tmodified:   part1_getting_started.ipynb\n",
    "#\tmodified:   part2_advanced_config.ipynb\n",
    "#\tmodified:   part2b_advanced_config.ipynb\n",
    "#\n",
    "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n",
    "```\n",
    "\n",
    "You can make a copy of those modified notebooks if you had significat changes, otherwise the easiest thing to do is to discard those changes.\n",
    "\n",
    "**ATTENTION** You will loose your local changes!\n",
    "\n",
    "```\n",
    "git checkout *.ipynb\n",
    "```\n",
    "\n",
    "At this point, you can update you copy of the repository:\n",
    "```\n",
    "git pull\n",
    "```\n",
    "\n",
    "\n",
    "## Update Conda Environment\n",
    "\n",
    "It is likely that you are running this notebook in the Conda environment `hls4ml-tutorial-cu`.\n",
    "\n",
    "If you did not do that yet, you should update the `hls4ml` packages with the latest changes in the working branch.\n",
    "\n",
    "```\n",
    "conda activate hls4ml-tutorial-cu\n",
    "pip uninstall hls4ml\n",
    "pip install git+https://github.com/GiuseppeDiGuglielmo/hls4ml.git@gdg/cosmetics#egg=hls4ml[profiling]\n",
    "```\n",
    "\n",
    "You may need to restart the Jupyter notebook.\n",
    "\n",
    "\n",
    "# Part 2b: Advanced Design Space Exploration\n",
    "\n",
    "In this notebook, we will leverage Python programming to scan the design space of our HLS model.\n",
    "\n",
    "## Setup\n",
    "\n",
    "As we did in the previous notebooks, let's import the libraries, call the magic functions, and setup the environment variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "Xilinx Vivado HLS is in the PATH\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import hls4ml\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.environ['PATH'] = '/opt/Xilinx/Vivado/2019.2/bin:' + os.environ['PATH']\n",
    "def is_tool(name):\n",
    "    from distutils.spawn import find_executable\n",
    "    return find_executable(name) is not None\n",
    "\n",
    "print('-----------------------------------')\n",
    "if not is_tool('vivado_hls'):\n",
    "    print('Xilinx Vivado HLS is NOT in the PATH')\n",
    "else:\n",
    "    print('Xilinx Vivado HLS is in the PATH')\n",
    "print('-----------------------------------')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the dataset and the model\n",
    "\n",
    "In [Part 1](part1_getting_started.ipynb), we saved the preprocessed dataset and model to files. Let's load them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "X_train_val = np.load('X_train_val.npy')\n",
    "X_test = np.load('X_test.npy')\n",
    "y_train_val = np.load('y_train_val.npy')\n",
    "y_test = np.load('y_test.npy', allow_pickle=True)\n",
    "classes = np.load('classes.npy', allow_pickle=True)\n",
    "\n",
    "# Load Keras model\n",
    "from tensorflow.keras.models import load_model\n",
    "model = load_model('model_1/KERAS_check_best_model.h5')\n",
    "y_keras = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DSE\n",
    "\n",
    "Let's combine everything together Python programming and `Precision` and `Reuse Factor` knobs.\n",
    "\n",
    "First we encapsulate in a function the creation of a hls4ml configuration dictionary and hls4ml model creation, compilation and built (C-synthesis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def process_hls4ml(model, fxd_w, fxd_i, rf, fpga_part, dse_hls_results):\n",
    "    # Generate a hls4ml configuration dictionary from the Keras model\n",
    "    config = hls4ml.utils.config_from_keras_model(model, granularity='Model')\n",
    "    \n",
    "    # Update the knobs\n",
    "    config['Model']['ReuseFactor'] = rf\n",
    "    config['Model']['Precision'] = 'ap_fixed<' + str(fxd_w) + ',' + str(fxd_i) + '>'\n",
    "\n",
    "    # Each hls4ml project / synthesis run must have its own working directory \n",
    "    output_dir = 'model_1/hls4ml_prj_rf' + str(rf) + '_fxd' + str(fxd_w) + '.' + str(fxd_i)\n",
    "\n",
    "    # Create an HLS model from the Keras model and the updated hls4ml configuration dictionary\n",
    "    hls_model = hls4ml.converters.convert_from_keras_model(model,\n",
    "                                                           hls_config=config,\n",
    "                                                           output_dir=output_dir,\n",
    "                                                           fpga_part=fpga_part)\n",
    "    _ = hls_model.compile()\n",
    "\n",
    "    # C-synthesis\n",
    "    start_time = time.time()\n",
    "    hls_results = hls_model.build(csim=False)\n",
    "    exec_time = time.time() - start_time\n",
    "\n",
    "    # Add extra information to the synthesis results\n",
    "    hls_results[\"ExecutionTime\"] = exec_time\n",
    "    hls_results[\"WbitsFixedPoint\"] = fxd_w\n",
    "    hls_results[\"IbitsFixedPoint\"] = fxd_i\n",
    "    hls_results[\"FPGApart\"] = fpga_part\n",
    "    \n",
    "    # Return results to the shared dictionary\n",
    "    dse_hls_results[rf] = hls_results\n",
    "    return hls_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the Python `multiprocessing` package to run multiple synthesis runs in parallel.\n",
    "\n",
    "**ATTENTION: Pay attention to the amount of memory and CPU cores/threads available on your host!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================= Memory Information =================================\n",
      "Total: 62.85GB\n",
      "Available: 60.15GB\n",
      "Used: 1.95GB\n",
      "Percentage: 4.3%\n",
      "================== SWAP ==================\n",
      "Total: 14.90GB\n",
      "Free: 11.01GB\n",
      "Used: 3.89GB\n",
      "Percentage: 26.1%\n",
      "====================================== CPU Info ======================================\n",
      "Physical cores: 8\n",
      "Total cores: 64\n",
      "Max Frequency: 0.00Mhz\n",
      "Min Frequency: 0.00Mhz\n",
      "Current Frequency: 1127.41Mhz\n",
      "CPU Usage Per Core:\n",
      "Core 0: 0.0%\n",
      "Core 1: 0.0%\n",
      "Core 2: 1.0%\n",
      "Core 3: 1.0%\n",
      "Core 4: 0.0%\n",
      "Core 5: 0.0%\n",
      "Core 6: 0.0%\n",
      "Core 7: 0.0%\n",
      "Core 8: 0.0%\n",
      "Core 9: 0.0%\n",
      "Core 10: 6.0%\n",
      "Core 11: 0.0%\n",
      "Core 12: 0.0%\n",
      "Core 13: 0.0%\n",
      "Core 14: 0.0%\n",
      "Core 15: 0.0%\n",
      "Core 16: 0.0%\n",
      "Core 17: 0.0%\n",
      "Core 18: 0.0%\n",
      "Core 19: 0.0%\n",
      "Core 20: 0.0%\n",
      "Core 21: 0.0%\n",
      "Core 22: 0.0%\n",
      "Core 23: 0.0%\n",
      "Core 24: 0.0%\n",
      "Core 25: 0.0%\n",
      "Core 26: 0.0%\n",
      "Core 27: 0.0%\n",
      "Core 28: 0.0%\n",
      "Core 29: 0.0%\n",
      "Core 30: 0.0%\n",
      "Core 31: 0.0%\n",
      "Core 32: 0.0%\n",
      "Core 33: 0.0%\n",
      "Core 34: 0.0%\n",
      "Core 35: 0.0%\n",
      "Core 36: 0.0%\n",
      "Core 37: 0.0%\n",
      "Core 38: 0.0%\n",
      "Core 39: 0.0%\n",
      "Core 40: 0.0%\n",
      "Core 41: 0.0%\n",
      "Core 42: 0.0%\n",
      "Core 43: 0.0%\n",
      "Core 44: 0.0%\n",
      "Core 45: 0.0%\n",
      "Core 46: 0.0%\n",
      "Core 47: 0.0%\n",
      "Core 48: 25.0%\n",
      "Core 49: 0.0%\n",
      "Core 50: 0.0%\n",
      "Core 51: 0.0%\n",
      "Core 52: 0.0%\n",
      "Core 53: 0.0%\n",
      "Core 54: 0.0%\n",
      "Core 55: 0.0%\n",
      "Core 56: 9.9%\n",
      "Core 57: 0.0%\n",
      "Core 58: 0.0%\n",
      "Core 59: 0.0%\n",
      "Core 60: 0.0%\n",
      "Core 61: 0.0%\n",
      "Core 62: 0.0%\n",
      "Core 63: 0.0%\n",
      "Total CPU Usage: 3.6%\n"
     ]
    }
   ],
   "source": [
    "import psutil\n",
    "\n",
    "def get_size(bytes, suffix=\"B\"):\n",
    "    \"\"\"\n",
    "    Scale bytes to its proper format\n",
    "    e.g:\n",
    "        1253656 => '1.20MB'\n",
    "        1253656678 => '1.17GB'\n",
    "    \"\"\"\n",
    "    factor = 1024\n",
    "    for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\"]:\n",
    "        if bytes < factor:\n",
    "            return f\"{bytes:.2f}{unit}{suffix}\"\n",
    "        bytes /= factor\n",
    "\n",
    "# Memory Information\n",
    "print(\"=\"*33, \"Memory Information\", \"=\"*33)\n",
    "\n",
    "# Get the memory details\n",
    "svmem = psutil.virtual_memory()\n",
    "print(f\"Total: {get_size(svmem.total)}\")\n",
    "print(f\"Available: {get_size(svmem.available)}\")\n",
    "print(f\"Used: {get_size(svmem.used)}\")\n",
    "print(f\"Percentage: {svmem.percent}%\")\n",
    "print(\"=\"*18, \"SWAP\", \"=\"*18)\n",
    "# Get the swap memory details (if exists)\n",
    "swap = psutil.swap_memory()\n",
    "print(f\"Total: {get_size(swap.total)}\")\n",
    "print(f\"Free: {get_size(swap.free)}\")\n",
    "print(f\"Used: {get_size(swap.used)}\")\n",
    "print(f\"Percentage: {swap.percent}%\")\n",
    "\n",
    "# CPU Information\n",
    "print(\"=\"*38, \"CPU Info\", \"=\"*38)\n",
    "\n",
    "# Number of cores\n",
    "print(\"Physical cores:\", psutil.cpu_count(logical=False))\n",
    "print(\"Total cores:\", psutil.cpu_count(logical=True))\n",
    "# CPU frequencies\n",
    "cpufreq = psutil.cpu_freq()\n",
    "print(f\"Max Frequency: {cpufreq.max:.2f}Mhz\")\n",
    "print(f\"Min Frequency: {cpufreq.min:.2f}Mhz\")\n",
    "print(f\"Current Frequency: {cpufreq.current:.2f}Mhz\")\n",
    "# CPU usage\n",
    "print(\"CPU Usage Per Core:\")\n",
    "for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)):\n",
    "    print(f\"Core {i}: {percentage}%\")\n",
    "print(f\"Total CPU Usage: {psutil.cpu_percent()}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also monitor the situation at runtime with the `htop` command in a console.\n",
    "\n",
    "Let's start with this initial configuration, but later you can change it as you prefer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.1 ms, sys: 566 ms, total: 578 ms\n",
      "Wall time: 18min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import multiprocessing\n",
    "\n",
    "# Choose the target FPGA chip\n",
    "target_fpga_part='xczu7ev-ffvc1156-2-e' # ZCU106\n",
    "#target_fpga_part='xczu3eg-sbva484-1-e' # Ultra96\n",
    "#target_fpga_part='xc7z020clg400-1' # Pynq-Z1\n",
    "#target_fpga_part='xc7z007sclg225-1' # Minized\n",
    "\n",
    "# DSE: Cartesian product of ReuseFactor and Precision\n",
    "reuse_factor_values = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1028]\n",
    "fxd_w_values = [16]\n",
    "#, 14, 12, 10, 8]\n",
    "fxd_i_values = [6]\n",
    "#, 4]\n",
    "\n",
    "# Append here the processes\n",
    "processes = list()\n",
    "\n",
    "# DSE results shared among processes\n",
    "manager = multiprocessing.Manager()\n",
    "dse_hls_results = manager.dict()\n",
    "\n",
    "# Swipe over the Precision and ReuseFactor values and spawn synthesis processes\n",
    "for fxd_w in fxd_w_values:\n",
    "    for fxd_i in fxd_i_values:\n",
    "        for rf in reuse_factor_values:\n",
    "            t = multiprocessing.Process(target=process_hls4ml, args=(model, fxd_w, fxd_i, rf, target_fpga_part, dse_hls_results))\n",
    "            processes.append(t)\n",
    "            t.start()\n",
    "\n",
    "# Wait for completion\n",
    "for t in processes:\n",
    "    t.join()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print DSE results on console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 892.5826549530029 s\n",
      "Estimated Clock Period: 5.195 ns\n",
      "Best/Worst Latency:     75 / 75\n",
      "Interval Min/Max:       16 / 16\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 271 (Aval. 1728)\n",
      "FF:                     53911 (Aval. 460800)\n",
      "LUT:                    116058 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 905.0179946422577 s\n",
      "Estimated Clock Period: 5.915 ns\n",
      "Best/Worst Latency:     244 / 244\n",
      "Interval Min/Max:       62 / 62\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 72 (Aval. 1728)\n",
      "FF:                     60508 (Aval. 460800)\n",
      "LUT:                    112394 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 912.8469898700714 s\n",
      "Estimated Clock Period: 5.915 ns\n",
      "Best/Worst Latency:     446 / 446\n",
      "Interval Min/Max:       123 / 123\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 39 (Aval. 1728)\n",
      "FF:                     59139 (Aval. 460800)\n",
      "LUT:                    108225 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 913.1918203830719 s\n",
      "Estimated Clock Period: 5.915 ns\n",
      "Best/Worst Latency:     13 / 13\n",
      "Interval Min/Max:       14 / 14\n",
      "BRAM_18K:               4 (Aval. 624)\n",
      "DSP48E:                 3917 (Aval. 1728)\n",
      "FF:                     24785 (Aval. 460800)\n",
      "LUT:                    87244 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 931.0812089443207 s\n",
      "Estimated Clock Period: 5.685 ns\n",
      "Best/Worst Latency:     135 / 135\n",
      "Interval Min/Max:       32 / 32\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 138 (Aval. 1728)\n",
      "FF:                     60435 (Aval. 460800)\n",
      "LUT:                    115443 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 962.3046152591705 s\n",
      "Estimated Clock Period: 4.882 ns\n",
      "Best/Worst Latency:     45 / 45\n",
      "Interval Min/Max:       8 / 8\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 537 (Aval. 1728)\n",
      "FF:                     42436 (Aval. 460800)\n",
      "LUT:                    124997 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 969.2283346652985 s\n",
      "Estimated Clock Period: 4.766 ns\n",
      "Best/Worst Latency:     25 / 25\n",
      "Interval Min/Max:       4 / 4\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 1069 (Aval. 1728)\n",
      "FF:                     34329 (Aval. 460800)\n",
      "LUT:                    130912 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 971.6928608417511 s\n",
      "Estimated Clock Period: 5.915 ns\n",
      "Best/Worst Latency:     13 / 13\n",
      "Interval Min/Max:       14 / 14\n",
      "BRAM_18K:               4 (Aval. 624)\n",
      "DSP48E:                 3917 (Aval. 1728)\n",
      "FF:                     24785 (Aval. 460800)\n",
      "LUT:                    87244 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 1004.8475732803345 s\n",
      "Estimated Clock Period: 4.729 ns\n",
      "Best/Worst Latency:     15 / 15\n",
      "Interval Min/Max:       2 / 2\n",
      "BRAM_18K:               3 (Aval. 624)\n",
      "DSP48E:                 2133 (Aval. 1728)\n",
      "FF:                     30582 (Aval. 460800)\n",
      "LUT:                    127780 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 1014.7339470386505 s\n",
      "Estimated Clock Period: 5.915 ns\n",
      "Best/Worst Latency:     877 / 877\n",
      "Interval Min/Max:       246 / 246\n",
      "BRAM_18K:               2 (Aval. 624)\n",
      "DSP48E:                 22 (Aval. 1728)\n",
      "FF:                     58701 (Aval. 460800)\n",
      "LUT:                    104502 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n",
      "-----------------------------------\n",
      "FPGA part: xczu7ev-ffvc1156-2-e\n",
      "Fixed-Point Word Size: 16\n",
      "Fixed-Point Integer-Part Size: 6\n",
      "Execution Time: 1104.1488993167877 s\n",
      "Estimated Clock Period: 4.145 ns\n",
      "Best/Worst Latency:     9 / 9\n",
      "Interval Min/Max:       1 / 1\n",
      "BRAM_18K:               4 (Aval. 624)\n",
      "DSP48E:                 3917 (Aval. 1728)\n",
      "FF:                     26905 (Aval. 460800)\n",
      "LUT:                    88335 (Aval. 230400)\n",
      "URAM:                   0 (Aval. 96)\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "def print_hls_results(hls_results):\n",
    "    print('-----------------------------------')\n",
    "    #print(hls_results) # Print hashmap\n",
    "    print(\"FPGA part: {}\".format(hls_results['FPGApart']))\n",
    "    print(\"Fixed-Point Word Size: {}\".format(hls_results['WbitsFixedPoint']))\n",
    "    print(\"Fixed-Point Integer-Part Size: {}\".format(hls_results['IbitsFixedPoint']))\n",
    "    print(\"Execution Time: {} s\".format(hls_results['ExecutionTime']))\n",
    "    print(\"Estimated Clock Period: {} ns\".format(hls_results['EstimatedClockPeriod']))\n",
    "    print(\"Best/Worst Latency:     {} / {}\".format(hls_results['BestLatency'], hls_results['WorstLatency']))\n",
    "    print(\"Interval Min/Max:       {} / {}\".format(hls_results['IntervalMin'], hls_results['IntervalMax']))\n",
    "    print(\"BRAM_18K:               {} (Aval. {})\".format(hls_results['BRAM_18K'], hls_results['AvailableBRAM_18K']))\n",
    "    print(\"DSP48E:                 {} (Aval. {})\".format(hls_results['DSP48E'], hls_results['AvailableDSP48E']))\n",
    "    print(\"FF:                     {} (Aval. {})\".format(hls_results['FF'], hls_results['AvailableFF']))\n",
    "    print(\"LUT:                    {} (Aval. {})\".format(hls_results['LUT'], hls_results['AvailableLUT']))\n",
    "    print(\"URAM:                   {} (Aval. {})\".format(hls_results['URAM'], hls_results['AvailableURAM']))\n",
    "    print('-----------------------------------')\n",
    "\n",
    "for rf in dse_hls_results.keys():\n",
    "    print_hls_results(dse_hls_results[rf])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a plot 2D function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "import matplotlib.ticker as plticker\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# available_resources: we are plotting some hw resources as DSPs; false if resource is like runtime, latency etc\n",
    "# show_values: show values as labels on the data points\n",
    "# show_precision: show fixed-point precision as a labels on the data points\n",
    "# xscale_log: log2 scale on x axis\n",
    "# yscale_log: log2 scale on y axis\n",
    "def plot_hls_results(dse_hls_results, resource_label, unit='HLS Estimates', available_resources=True, show_values=False, show_precision=True, xscale_log=False, yscale_log=False):\n",
    "\n",
    "    # Reorder the results according to the ReuseFactor (dictionary key)\n",
    "    dse_hls_results_ordered = collections.OrderedDict(sorted(dse_hls_results.items(), key=lambda x:x[0], reverse=False))\n",
    "\n",
    "    # Fonts\n",
    "    SMALL_SIZE = 8\n",
    "    MEDIUM_SIZE = 10\n",
    "    BIGGER_SIZE = 14\n",
    "    plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes\n",
    "    plt.rc('axes', titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
    "    plt.rc('axes', labelsize=BIGGER_SIZE)     # fontsize of the x and y labels\n",
    "    plt.rc('xtick', labelsize=BIGGER_SIZE)    # fontsize of the tick labels\n",
    "    plt.rc('ytick', labelsize=BIGGER_SIZE)    # fontsize of the tick labels\n",
    "    plt.rc('legend', fontsize=BIGGER_SIZE)    # legend fontsize\n",
    "    plt.rc('figure', titlesize=BIGGER_SIZE)   # fontsize of the figure title\n",
    "    \n",
    "    # Extract values of the x and y axes \n",
    "    reuse_factor_x_axis = np.array(list(dse_hls_results_ordered.keys()))\n",
    "    resource_usage_y_axis = []\n",
    "    fxd_w_precision = []\n",
    "    fxd_i_precision = []\n",
    "    resource_available_y_axis=[]\n",
    "    fpga_parts=[]\n",
    "    \n",
    "    for i, d in dse_hls_results_ordered.items():\n",
    "        resource_usage_y_axis.append(int(dse_hls_results_ordered[i][resource_label]))\n",
    "    if (available_resources):\n",
    "        for i, d in dse_hls_results_ordered.items():\n",
    "            resource_available_y_axis.append(int(dse_hls_results_ordered[i]['Available' + resource_label]))\n",
    "    for i, d in dse_hls_results_ordered.items():\n",
    "        fxd_w_precision.append(int(dse_hls_results_ordered[i]['WbitsFixedPoint']))\n",
    "    for i, d in dse_hls_results_ordered.items():\n",
    "        fxd_i_precision.append(int(dse_hls_results_ordered[i]['IbitsFixedPoint']))\n",
    "    for i, d in dse_hls_results_ordered.items():\n",
    "        fpga_parts.append(dse_hls_results_ordered[i]['FPGApart'])\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    ax.set_xlabel('Reuse Factor')\n",
    "    \n",
    "    ax.set_ylabel(resource_label + ' (' + unit + ')')\n",
    "    ax.plot(reuse_factor_x_axis, resource_usage_y_axis, linestyle='--', marker='o')\n",
    "\n",
    "    if (available_resources):\n",
    "        ax.axhline(y=resource_available_y_axis[0], color='r', linestyle='-', label = resource_label + ' (Available)')\n",
    "        ax.legend(loc='upper right')\n",
    "\n",
    "    ax.set_xticks(reuse_factor_x_axis)    \n",
    "    ax.grid(True)\n",
    "    plt.figtext(0.90, 0.90, 'hls4ml', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)\n",
    "    plt.figtext(0.4, 0.90, fpga_parts[0], wrap=True, horizontalalignment='right', fontsize=14)\n",
    "    \n",
    "    if xscale_log: ax.set_xscale('log', base=2)\n",
    "    if yscale_log: ax.set_yscale('log', base=2)\n",
    "    \n",
    "    if show_values:\n",
    "        for x,y in zip(reuse_factor_x_axis,resource_usage_y_axis):\n",
    "            label = \"{:d}\".format(y)\n",
    "            plt.annotate(label, # this is the text\n",
    "                     (x,y), # this is the point to label\n",
    "                     textcoords=\"offset points\", # how to position the text\n",
    "                     xytext=(0,10), # distance from text to points (x,y)\n",
    "                     ha='center') # horizontal alignment can be left, right or center\n",
    "    if show_precision:\n",
    "        for x,y,w,i in zip(reuse_factor_x_axis,resource_usage_y_axis,fxd_w_precision,fxd_i_precision):\n",
    "            label = '<{},{}>'.format(w,i)\n",
    "            plt.annotate(label, # this is the text\n",
    "                     (x,y), # this is the point to label\n",
    "                     textcoords=\"offset points\", # how to position the text\n",
    "                     xytext=(0,10), # distance from text to points (x,y)\n",
    "                     ha='center') # horizontal alignment can be left, right or center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot some of the resources from the previous DSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hls_results(dse_hls_results, 'ExecutionTime', unit='s', available_resources=False, xscale_log=True)\n",
    "plot_hls_results(dse_hls_results, 'DSP48E', xscale_log=True)\n",
    "plot_hls_results(dse_hls_results, 'BRAM_18K', xscale_log=True)\n",
    "plot_hls_results(dse_hls_results, 'FF', xscale_log=True)\n",
    "plot_hls_results(dse_hls_results, 'LUT', xscale_log=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save DSE results on file for future reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "json = json.dumps(dse_hls_results.copy())\n",
    "f = open(\"dse_hls_results.json\",\"w\")\n",
    "f.write(json)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load DSE results from file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'16': {'EstimatedClockPeriod': '5.195', 'BestLatency': '75', 'WorstLatency': '75', 'IntervalMin': '16', 'IntervalMax': '16', 'BRAM_18K': '2', 'DSP48E': '271', 'FF': '53911', 'LUT': '116058', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 892.5826549530029, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '64': {'EstimatedClockPeriod': '5.915', 'BestLatency': '244', 'WorstLatency': '244', 'IntervalMin': '62', 'IntervalMax': '62', 'BRAM_18K': '2', 'DSP48E': '72', 'FF': '60508', 'LUT': '112394', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 905.0179946422577, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '128': {'EstimatedClockPeriod': '5.915', 'BestLatency': '446', 'WorstLatency': '446', 'IntervalMin': '123', 'IntervalMax': '123', 'BRAM_18K': '2', 'DSP48E': '39', 'FF': '59139', 'LUT': '108225', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 912.8469898700714, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '512': {'EstimatedClockPeriod': '5.915', 'BestLatency': '13', 'WorstLatency': '13', 'IntervalMin': '14', 'IntervalMax': '14', 'BRAM_18K': '4', 'DSP48E': '3917', 'FF': '24785', 'LUT': '87244', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 913.1918203830719, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '32': {'EstimatedClockPeriod': '5.685', 'BestLatency': '135', 'WorstLatency': '135', 'IntervalMin': '32', 'IntervalMax': '32', 'BRAM_18K': '2', 'DSP48E': '138', 'FF': '60435', 'LUT': '115443', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 931.0812089443207, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '8': {'EstimatedClockPeriod': '4.882', 'BestLatency': '45', 'WorstLatency': '45', 'IntervalMin': '8', 'IntervalMax': '8', 'BRAM_18K': '2', 'DSP48E': '537', 'FF': '42436', 'LUT': '124997', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 962.3046152591705, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '4': {'EstimatedClockPeriod': '4.766', 'BestLatency': '25', 'WorstLatency': '25', 'IntervalMin': '4', 'IntervalMax': '4', 'BRAM_18K': '2', 'DSP48E': '1069', 'FF': '34329', 'LUT': '130912', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 969.2283346652985, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '1028': {'EstimatedClockPeriod': '5.915', 'BestLatency': '13', 'WorstLatency': '13', 'IntervalMin': '14', 'IntervalMax': '14', 'BRAM_18K': '4', 'DSP48E': '3917', 'FF': '24785', 'LUT': '87244', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 971.6928608417511, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '2': {'EstimatedClockPeriod': '4.729', 'BestLatency': '15', 'WorstLatency': '15', 'IntervalMin': '2', 'IntervalMax': '2', 'BRAM_18K': '3', 'DSP48E': '2133', 'FF': '30582', 'LUT': '127780', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 1004.8475732803345, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '256': {'EstimatedClockPeriod': '5.915', 'BestLatency': '877', 'WorstLatency': '877', 'IntervalMin': '246', 'IntervalMax': '246', 'BRAM_18K': '2', 'DSP48E': '22', 'FF': '58701', 'LUT': '104502', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 1014.7339470386505, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}, '1': {'EstimatedClockPeriod': '4.145', 'BestLatency': '9', 'WorstLatency': '9', 'IntervalMin': '1', 'IntervalMax': '1', 'BRAM_18K': '4', 'DSP48E': '3917', 'FF': '26905', 'LUT': '88335', 'URAM': '0', 'AvailableBRAM_18K': '624', 'AvailableDSP48E': '1728', 'AvailableFF': '460800', 'AvailableLUT': '230400', 'AvailableURAM': '96', 'ExecutionTime': 1104.1488993167877, 'WbitsFixedPoint': 16, 'IbitsFixedPoint': 6, 'FPGApart': 'xczu7ev-ffvc1156-2-e'}}\n"
     ]
    }
   ],
   "source": [
    "import json \n",
    "\n",
    "with open('dse_hls_results.json') as json_file: \n",
    "    data = json.load(json_file) \n",
    "  \n",
    "    # Print the type of data variable \n",
    "    print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "- Use `Pool` Python package to allocate a number of jobs non bigger than the number of CPU cores/threads similarly to the `-j` option in [GNU Parallel](https://www.gnu.org/software/parallel).\n",
    "- DSE at the same time over `Precision` and `ReuseFactor`.\n",
    "- 3D plotting (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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