Naive_Model_Estimator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Model Power Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following model(s) uses two files as inputs to perform the calculation, and a third file that contains the true values to compare the results with.\n",
    "\n",
    "### Input files are:\n",
    "True data values file:\n",
    "- Benchmarks data (code, power)\n",
    "- Instructions data (instruction : (power, cycles))\n",
    "\n",
    "Profiling file:\n",
    "- Benchmark profile (code, (instruction : instruction_count))\n",
    "\n",
    "\n",
    "### Models\n",
    "Two naive models are introduced:\n",
    "- model 1: <br>\n",
    "    $P_{avg} = \\frac{1}{\\sum I_{count}} \\sum \\limits_{i=1}^{N} P_{i} \\cdot I_{count}$\n",
    "- model 2: <br>\n",
    "    $P_{avg} = \\frac{1}{\\sum I_{count} \\cdot Cycles_{i}} \\sum \\limits_{i=1}^{N} P_{i} \\cdot I_{count} \\cdot Cycles_{i}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "inst_regex = {\n",
    "        \"mov\": \"MOV\",\n",
    "        \"mvn\": \"MOV\",\n",
    "        \"ldr\": \"LDR\",\n",
    "        \"bl\": \"BRANCH\",\n",
    "        \"push\": \"PUSH\",\n",
    "        \"add\": \"AND\",\n",
    "        \"sub\": \"SUB\",\n",
    "        \"str\": \"STR\",\n",
    "        \"lsl\": \"LSL\",\n",
    "        \"orr\": \"EOR\",\n",
    "        \"ldrd\": \"LDR\",\n",
    "        \"mul\": \"MUL\",\n",
    "        \"umull\": \"MUL\",\n",
    "        \"lsr\": \"LSR\",\n",
    "        \"ldm\": \"LDR\",\n",
    "        \"stm\": \"STR\",\n",
    "        \"stmia\": \"STR\",\n",
    "        \"asr\": \"ASR\",\n",
    "        \"b\": \"BRANCH\",\n",
    "        \"cmp\": \"CMP\",\n",
    "        \"blt\": \"BRANCH\",\n",
    "        \"bge\": \"BRANCH\",\n",
    "        \"nop\": \"MOV\",\n",
    "        \"pop\": \"POP\",\n",
    "        \"bx\": \"BRANCH\",\n",
    "        \"bgt\": \"BRANCH\",\n",
    "        \"ble\": \"BRANCH\",\n",
    "        \"bne\": \"BRANCH\"\n",
    "    }\n",
    "\n",
    "def naive_model_1(inst_data_dict, benchmark_prof_dict):\n",
    "    \"\"\"This function represent the estimation of the average power according to the first Naive model.\n",
    "        Inputs:\n",
    "        - inst_data_dict: contains the power average per inst and nb of cycles. ({<inst>: { <power>: #power, <cycles>: #cycles}})\n",
    "        - benchmark_prof_dict: contains profling data of each benchmark, occurence of each instruction in a code.\"\"\"\n",
    "    \n",
    "    power_avg = 0\n",
    "    denominator = 0\n",
    "\n",
    "    for inst, count in benchmark_prof_dict.items():\n",
    "        power_avg += inst_data_dict[inst_regex[inst]]['power']*count\n",
    "        denominator += count\n",
    "    return power_avg/denominator\n",
    "\n",
    "\n",
    "def naive_model_2(inst_data_dict, benchmark_prof_dict):\n",
    "    \n",
    "    power_avg = 0\n",
    "    denominator = 0\n",
    "\n",
    "    for inst, count in benchmark_prof_dict.items():\n",
    "        power_avg += inst_data_dict[inst_regex[inst]]['voltage']*count*inst_data_dict[inst_regex[inst]]['cycles']\n",
    "        denominator += count*inst_data_dict[inst_regex[inst]]['cycles']\n",
    "    return power_avg/denominator\n",
    "\n",
    "def read_json_file(file_path):\n",
    "    with open(file_path, 'r') as f:\n",
    "        return json.load(f)\n",
    "    \n",
    "def relative_error(true_list, estimation_list):\n",
    "    relative_error_list = []\n",
    "    for i in range(len(estimation_list)):\n",
    "        relative_error_list.append(100*abs(estimation_list[i]-true_list[i])/true_list[i])\n",
    "    return relative_error_list\n",
    "\n",
    "def save_results_as_json_file(file_path, data):\n",
    "    with open(file_path, 'w') as j_file:\n",
    "        json.dump(data, j_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply Estimation Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy files preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inst_data = read_json_file(\"energy_files/voltage_instruction_data.json\")\n",
    "bench_prof = read_json_file(\"energy_files/benchmarks_profile.json\")\n",
    "bench_true_data = read_json_file(\"results/benchmarks_data.json\")\n",
    "\n",
    "estimation_benchmark_list = [\"matrix_conv\", \"factorial\", \"matrix_mult\", \"matrix_mult_3x3\", \"fibonacci\", \"fibonacci_40\", \"median_filter\", \"qsort\", \"qsort_14\"]\n",
    "true_benchmark_list = [\"matrix_conv_50MHz\", \"Factorial_100MHz\", \"matrix_mult_100MHz\", \"matrix_mult_50MHz_3x3\", \"fibonacci_100MHz\", \"fibonacci_100MHz_40\", \"median_filter_50MHz\", \"qsort_50MHz\", \"qsort_50MHz_14\"]\n",
    "\n",
    "volt_dict = {\n",
    "    \"voltage\": {},\n",
    "    \"cycles\": {}\n",
    "}\n",
    "\n",
    "for e in range(len(estimation_benchmark_list)):\n",
    "    volt_dict['voltage'][estimation_benchmark_list[e]] = naive_model_2(inst_data, bench_prof[estimation_benchmark_list[e]])\n",
    "    volt_dict['cycles'][estimation_benchmark_list[e]] = bench_true_data[true_benchmark_list[e]]['cycles']\n",
    "save_results_as_json_file(\"energy_files/estimation_voltage_benchmark.json\", volt_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inst_data = read_json_file(\"results/instructions_data.json\")\n",
    "bench_prof = read_json_file(\"results/benchmarks_profile.json\")\n",
    "bench_true_data = read_json_file(\"results/benchmarks_data.json\")\n",
    "\n",
    "estimation_benchmark_list = [\"matrix_conv\", \"factorial\", \"matrix_mult\", \"matrix_mult_3x3\", \"fibonacci\", \"fibonacci_40\", \"median_filter\", \"qsort\", \"qsort_14\"]\n",
    "true_benchmark_list = [\"matrix_conv_50MHz\", \"Factorial_100MHz\", \"matrix_mult_100MHz\", \"matrix_mult_50MHz_3x3\", \"fibonacci_100MHz\", \"fibonacci_100MHz_40\", \"median_filter_50MHz\", \"qsort_50MHz\", \"qsort_50MHz_14\"]\n",
    "\n",
    "estimated_power_avg_1 = [naive_model_1(inst_data, bench_prof[e]) for e in estimation_benchmark_list]\n",
    "estimated_power_avg_2 = [naive_model_2(inst_data, bench_prof[e]) for e in estimation_benchmark_list]\n",
    "true_power_avg = [bench_true_data[d]['power'] for d in true_benchmark_list]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "assert len(estimated_power_avg_1) == len(true_power_avg)\n",
    "assert len(estimated_power_avg_2) == len(true_power_avg)\n",
    "\n",
    "relative_error_1 = relative_error(true_power_avg, estimated_power_avg_1)\n",
    "relative_error_2 = relative_error(true_power_avg, estimated_power_avg_2)\n",
    "\n",
    "avg_rel_error_1 = round(mean(relative_error_1), 2)\n",
    "avg_rel_error_2 = round(mean(relative_error_2), 2)\n",
    "\n",
    "x = np.arange(len(true_power_avg)) # calculate nb of columns\n",
    "\n",
    "w = 0.15\n",
    "width = 0.1\n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(x-w/2, relative_error_1, width=width, color='b', align='center')\n",
    "ax.bar(x+w/2, relative_error_2, width=width, color='g', align='center')\n",
    "\n",
    "for i, v in enumerate(relative_error_1):\n",
    "    plt.text(i-0.2, v+0.1, str(round(v, 2))+\"%\")\n",
    "\n",
    "for i, v in enumerate(relative_error_2):\n",
    "    plt.text(i, v+0.1, str(round(v, 2))+\"%\")\n",
    "\n",
    "plt.title(f'Relative Error (avg 1 = {avg_rel_error_1}%, avg 2 = {avg_rel_error_2}%)')\n",
    "plt.xticks(x, estimation_benchmark_list)\n",
    "plt.ylabel('Error (%)')\n",
    "plt.legend(['model 1', 'model 2'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1 (Cycles not considered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "assert len(estimated_power_avg_1) == len(true_power_avg)\n",
    "\n",
    "relative_error_dict = {}\n",
    "for i in range(len(estimated_power_avg_1)):\n",
    "    relative_error_dict[estimation_benchmark_list[i]] = 100*abs(estimated_power_avg_1[i]-true_power_avg[i])/true_power_avg[i]\n",
    "\n",
    "avg_rel_error = round(mean(relative_error_dict.values()), 2)\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.title(f\"Relative Error (avg = {avg_rel_error}%)\")\n",
    "plt.bar(relative_error_dict.keys(), relative_error_dict.values(), width=0.4, align= 'center')\n",
    "for i, v in enumerate(relative_error_dict.values()):\n",
    "    plt.text(i-0.08, v+0.1, str(round(v, 2))+\"%\")\n",
    "plt.xlabel(\"Benchmark\")\n",
    "plt.ylabel(\"Error (%)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2 (multiplied by cycles weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "assert len(estimated_power_avg_2) == len(true_power_avg)\n",
    "\n",
    "relative_error_dict = {}\n",
    "for i in range(len(estimated_power_avg_2)):\n",
    "    relative_error_dict[estimation_benchmark_list[i]] = 100*abs(estimated_power_avg_2[i]-true_power_avg[i])/true_power_avg[i]\n",
    "\n",
    "avg_rel_error = round(mean(relative_error_dict.values()), 2)\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.title(f\"Relative Error (avg = {avg_rel_error}%)\")\n",
    "plt.bar(relative_error_dict.keys(), relative_error_dict.values(), width=0.4, align= 'center')\n",
    "for i, v in enumerate(relative_error_dict.values()):\n",
    "    plt.text(i-0.08, v+0.1, str(round(v, 2))+\"%\")\n",
    "plt.xlabel(\"Benchmark\")\n",
    "plt.ylabel(\"Error (%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nNe3atdO333570bY3bdrUOk9AzXQlvVunTh3FxsZq0qRJ2rZtm77//nulp6frpptukoeHh8O1oLNnzyojI+Oy71N2z59RM9Wmfps+fbq2bt1qHcdTU1MlSQsXLtRLL71UqesCagMCN1SLm2++WZs3b9by5cu1a9cu/e1vf1NGRoZDTWRkpLZt26acnBz99NNPFfpE+rJlyzRr1izNnz9f9957r0aMGKH4+PiL/tAsS1JSkvLz8/XII49o8+bN2r17t+bNm6ecnBxJ0ogRI/TKK69o4cKFysnJ0fPPP6/s7Gw9/fTTFdoHXbp0UVhYmAYMGKAmTZo43GYO51O3bl3Vq1dPM2fO1J49e5Senq7k5GSHmv79+yssLEx9+vTRunXrtG/fPi1evFgbNmyQ9FtP79+/X9nZ2frpp59UWFio2NhYtWrVSgMGDFBWVpY2bdqkxx57TF27dq3QVwM0btxYHh4eeuONN7Rv3z4tWbJEEyZMqNR9AOfgzO+fV+vuu+/WvHnztHbtWm3fvl3x8fFyc3O7ZH1ISIi8vb2VlpamvLw8nTx58pqN7XpCb11db02cOFErV67Uvn37tHPnTr3++uuaN2+eHn30Uatm2LBhatSokUaPHq3JkyeruLhYf/7znyu0DFxb69at06RJk7Rr1y6lpKRo0aJF1rldZRyrK+M8YuzYsfrggw80duxY7dy5U9u3b9crr7xSuTsCVeJ66rewsDC1bNnS4SH9du7apEkTSVL37t3VokULDRw4UFu3btXy5cs1evRoJSYmytPTs8LrhD2JiYk6fvy4+vfvr4yMDO3du1fLly/X4MGDVVxcLD8/PyUkJGjEiBFKT0/XN998o0GDBsnV9dKXl5o2baqzZ89af6vMmzdPM2bMqMKtKltkZKTWrFmj//u//9NPP/0kSfrLX/6i9evXKykpybqz77PPPlNSUlI1jxZ2Xa53ly5dqunTpys7O1s//PCD5s6dq5KSEt16663y9fXV8OHDNWLECKWlpenbb7/VkCFDdObMGSUkJJS73rKuA6D2q0n9lpubq+zsbO3Zs0eStH37docP5TRu3NjhOH7LLbdI+i1UbNiwoZ3dBNRKBG6oFk888YQefPBB9evXTx06dNCxY8f05JNPOtQMGTJEt956q9q3b6/69etfdFfZpRw9elQJCQkaN26c2rVrJ0l68cUXFRoaqmHDhl329fXq1VN6erpOnz6trl27Kjo6Wu+884719RZ/+tOflJycrOeee06tWrVSWlqalixZUuHfoXFxcVH//v21detW7m6rAVxdXfXhhx8qMzNTLVu21LPPPqtXX33VocbDw0MrVqxQSEiI7rvvPrVq1Uovv/yydXG3b9++6tGjh+666y7Vr19fH3zwgVxcXPTZZ5+pbt266tKli2JjY3XjjTdq4cKFFRpf/fr1NWfOHC1atEgtWrSwfh8JtY8zv39erVGjRqlr1666//771atXL/Xp08fhK3suVKdOHU2fPl1vv/22wsPD9cADD1yzsV1P6K2r662CggI9+eSTuu2229SxY0ctXrxY77//vv74xz9KkubOnavU1FTNmzdPderUka+vr95//3298847+vzzz69oGbj2nnvuOW3evFlt27bV3//+d02ePFlxcXGSVCnH6so4j+jWrZsWLVqkJUuWKCoqSnfffbc2bdpUeTsBVYZ+c+Tm5qalS5fKzc1NMTExevTRR/XYY49p/Pjx12R9KF/pN7oUFxere/fuatWqlZ555hkFBQVZF4pfffVVde7cWb1791ZsbKw6deqk6OjoSy6zTZs2mjx5sl555RW1bNlS8+fPd4qvTR4/fry+//573XTTTdbvZbdu3VpfffWVdu3apc6dO6tt27YaM2aMwsPDq3m0qAzl9W5QUJA+/vhj3X333WrevLlmzJihDz74QLfddpsk6eWXX1bfvn01cOBAtWvXTnv27NHy5ctVt27dctdZ1nUAXB9qSr/NmDFDbdu21ZAhQyT9doNA27ZttWTJEpt7ALg+uZiq/kJsAAAAAAAAAKhm3bp1U1RUlKZOnVrdQ8F1gH4Daj/ucAMAAAAAAAAAAABsIHDDdWfYsGHy8/Mr83Etv9YKAGo63j9xrThzb11qXH5+flq7dm21jg0Aqtv8+fMv+R5Z+rVYQGXgeAwAFcdxGqh6fKUkrjtHjhxRfn5+mfMCAgIUEhJSxSMCgJqB909cK87cW6U/Hl6WBg0ayNvbuwpHAwDO5dSpU8rLyytznru7uyIiIqp4RKitOB4DQMVxnAaqHoEbAAAAAAAAAAAAYANfKQkAAAAAAAAAAADYQOAGAAAAAAAAAAAA2EDgBgAAAAAAAAAAANhA4AYAAAAAAAAAAADYQOAGAAAAAAAAAAAA2EDgBgAAAAAAAAAAANhA4AYAAAAAAAAAAADYQOAGAAAAAAAAAAAA2PD/ACGFJAyyGiFbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(len(estimation_benchmark_list))\n",
    "\n",
    "w = 0.15\n",
    "width = 0.1\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(x-w, estimated_power_avg_1, width=width, color='b', align='center', label='model 1')\n",
    "ax.bar(x, estimated_power_avg_2, width=width, color='g', align='center', label='model 2')\n",
    "ax.bar(x+w, true_power_avg, width=width, color='r', align='center', label='true')\n",
    "\n",
    "for i, v in enumerate(true_power_avg):\n",
    "    plt.text(i, v+1e3, str(round(v, 2)))\n",
    "\n",
    "plt.title('Power Estimation VS True Measurements')\n",
    "plt.xticks(x, estimation_benchmark_list)\n",
    "plt.ylabel('Power (uW)')\n",
    "plt.legend(bbox_to_anchor=(1.0, 1.0), loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}