{"id":3384,"date":"2026-04-21T10:42:47","date_gmt":"2026-04-21T08:42:47","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/energy-consumption-comparison\/"},"modified":"2026-08-25T12:02:45","modified_gmt":"2026-08-25T10:02:45","slug":"energy-consumption-comparison","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/energy-consumption-comparison\/","title":{"rendered":"Energy consumption of TensorFlow and Neural Designer"},"content":{"rendered":"<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 14px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,1200px);margin:0 auto}.ndb-lead{font-size:18px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 22px}.ndb-lead a{color:#2d799f;font-weight:600}.ndb-highlight{margin:0 0 36px;padding:28px 34px;border-radius:18px;background:linear-gradient(135deg,#56a1c8 0%,#245e80 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14px;font-size:16.5px;line-height:1.62;color:#33424f}.ndb-card a{color:#2d799f;font-weight:500}.ndb-card ul{margin:0 0 14px;padding-left:22px}.ndb-card li{margin:5px 0;font-size:16px;line-height:1.5;color:#33424f}.ndb-card img:not([src$=\".svg\"]):not([data-src$=\".svg\"]){display:block;width:auto;max-width:min(560px,100%);height:auto;margin:22px auto;border-radius:12px;box-shadow:0 12px 28px rgba(0,18,51,.12)}.ndb-card th img[src$=\".svg\"],.ndb-card th img[data-src$=\".svg\"],.ndb-card img[src$=\".svg\"],.ndb-card img[data-src$=\".svg\"]{display:inline-block;max-width:24px;height:auto;margin:0 6px -4px 0;box-shadow:none}.ndb-card table{border-collapse:separate;border-spacing:0;width:100%;max-width:100%;margin:22px 0;font-size:15px;background:#fbfcfd;border-radius:12px;overflow:hidden;box-shadow:0 10px 24px rgba(0,18,51,.08)}.ndb-card th,.ndb-card td{padding:11px 16px;border-bottom:1px solid #e6ecf0;text-align:left;vertical-align:top}.ndb-card thead th{background:#12354b;color:#fff;font-weight:600;text-align:center}.ndb-card tbody th{background:#e9f1f6;color:#12354b;font-weight:600}.ndb-card td{color:#33424f}.ndb-card table ul{margin:0;padding-left:18px}.ndb-card table li{font-size:14px}.ndb-card pre{margin:22px 0;padding:22px 24px;background:#0b1830!important;color:#e6eef5!important;border-radius:14px;overflow-x:auto;font-family:Consolas,Menlo,monospace;font-size:12.5px;line-height:1.5;white-space:pre}.ndb-card--accent{background:transparent}@media(max-width:820px){.ndb-card{padding:0}.ndb-card h2{font-size:21px}.ndb-toc{gap:8px}}@media(max-width:640px){.ndb{padding:12px 14px}}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><div class=\"ndb-lead\"><\/p>\n<p><a href=\"https:\/\/tensorflow.org\/\">TensorFlow<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>\u00a0are popular machine learning platforms developed by\u00a0<a href=\"https:\/\/research.google\/teams\/brain\/\">Google<\/a> and\u00a0<a href=\"https:\/\/www.artelnics.com\/\">Artelnics<\/a>, respectively.<\/p>\n<p>Although all those frameworks are based on neural networks, they present essential differences in functionality, usability, performance, consumption, etc.<\/p>\n<p>This post compares the energy consumption of TensorFlow and Neural Designer using the GPU for an approximation benchmark.<\/p>\n<p data-start=\"50\" data-end=\"154\">In this article, we outline all the steps required to reproduce the results using Neural Designer (<a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">download<\/a>)<\/p>\n<\/div><div class=\"ndb-highlight\"><p>As we will see, Neural Designer consumes <b>42<\/b>% less than its competitor machine learning platform.<\/p>\n<\/div><ul class=\"ndb-toc\"><li><a href=\"#Introduction\">Introduction<\/a><\/li><li><a href=\"#BenchmarkApplication\">Benchmark application<\/a><\/li><li><a href=\"#ReferenceComputer\">Reference computer<\/a><\/li><li><a href=\"#Referenceelectricityconsumptionmeter\">Reference electricity consumption meter<\/a><\/li><li><a href=\"#Results\">Results<\/a><\/li><li><a href=\"#Conclusions\">Conclusions<\/a><\/li><\/ul><div class=\"ndb-card\" id=\"Introduction\"><h2>Introduction<\/h2><p>Two of the most essential features of machine learning platforms are their training speed and the total amount of energy consumed during this process.<\/p>\n<p>In most cases, modeling huge data sets is very expensive in computational terms, which leads to a high economic cost of neural network training and a high environmental impact.<\/p>\n<p>Thus, this article aims to measure the GPU energy consumption of TensorFlow and Neural Designer for a benchmark application. Also, a couple of instructions are given to enable anyone to repeat this one or a similar benchmark and check on their own the fantastic results obtained when Neural Designer is used.<\/p>\n<p>The following table summarizes the technical features of these tools that might impact their GPU performance.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00a0<\/th>\n<th>TensorFlow<\/th>\n<th>Neural Designer<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Written in<\/th>\n<td>C++, CUDA, Python<\/td>\n<td>C++, CUDA<\/td>\n<\/tr>\n<tr>\n<th>Interface<\/th>\n<td>Python<\/td>\n<td>Graphical User Interface<\/td>\n<\/tr>\n<tr>\n<th>Differentiation<\/th>\n<td>Automatic<\/td>\n<td>Analytical<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The above table shows that TensorFlow is programmed in C++ and Python, whereas Neural Designer is entirely programmed in C++.<\/p>\n<p>Interpreted languages like Python have advantages over compiled languages like C ++, such as their ease of use.<\/p>\n<p>However, the performance of Python is generally lower than that of C++. Indeed, Python takes significant time to interpret sentences during the program&#8217;s execution.<\/p>\n<p>On the other hand, TensorFlow uses automatic differentiation, while Neural Designer uses analytical differentiation.<\/p>\n<p>As before, automatic differentiation has some advantages over analytical differentiation. Indeed, it simplifies obtaining the gradient for new architectures or loss indices.<\/p>\n<p>However, the performance of automatic differentiation is, in general, lower than that of analytical differentiation:<br \/>The first derives the gradient during the program&#8217;s execution, while the second has that formula pre-calculated.<\/p>\n<p>Next, we use TensorFlow and Neural Designer to measure the energy consumption for a benchmark problem on a reference computer. The results produced by these platforms are then compared.<\/p>\n<\/div><div class=\"ndb-card\" id=\"BenchmarkApplication\"><h2>Benchmark application<\/h2><p>The first step is to choose a benchmark application that is general enough to conclude the performance of the machine learning platforms. As previously stated, we will train a neural network that approximates a set of input-target samples.<\/p>\n<p>In this regard, an approximation application is defined by a data set, a neural network, and an associated training strategy.<br \/>The following table uniquely defines these three components.<\/p>\n<table>\n<tbody>\n<tr>\n<th>Data set<br \/><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/data_set.svg\"  alt=\"Data Set\"\/><\/th>\n<td>\n<ul>\n<li>Benchmark: Rosenbrock<\/li>\n<li>Inputs number: 1000<\/li>\n<li>Targets number: 1<\/li>\n<li>Samples number: 1000000<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Neural network<br \/><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/neural_network.svg\"  alt=\"Neural Network\"\/><\/th>\n<td>\n<ul>\n<li>Layers number: 2<\/li>\n<li>Layer 1:\n<ul style=\"list-style-type: none;\">\n<li>-Type: Perceptron (Dense)<\/li>\n<li>-Inputs number: 1000<\/li>\n<li>-Neurons number: 1000<\/li>\n<li>-Activation function: Hyperbolic tangent (tanh)<\/li>\n<\/ul>\n<\/li>\n<li>Layer 2:\n<ul style=\"list-style-type: none;\">\n<li>-Type: Perceptron (Dense)<\/li>\n<li>-Inputs number: 1000<\/li>\n<li>-Neurons number: 1<\/li>\n<li>-Activation function: Linear<\/li>\n<\/ul>\n<\/li>\n<li>Initialization: Random uniform [-1,1]<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Training strategy<br \/><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/training_strategy.svg\"  alt=\"Training Strategy\"\/><\/th>\n<td>\n<ul>\n<li>Loss index:\n<ul style=\"list-style-type: none;\">\n<li>-Error: Mean Squared Error (MSE)<\/li>\n<li>-Regularization: None<\/li>\n<\/ul>\n<\/li>\n<li>Optimization algorithm:\n<ul style=\"list-style-type: none;\">\n<li>-Algorithm: Adaptive Moment Estimation (Adam)<\/li>\n<li>-Batch size: 1000<\/li>\n<li>-Maximum epochs: 20000<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Once the TensorFlow and Neural Designer applications have been created, we must run them.<\/p>\n<\/div><div class=\"ndb-card\" id=\"ReferenceComputer\"><h2>Reference computer<\/h2><p>The next step is to choose the computer in which the neural network will be trained with TensorFlow and Neural Designer.<br \/>The table below shows the features of the computer used for this instance.<\/p>\n<table>\n<tbody>\n<tr>\n<th>Operating system:<\/th>\n<td>Windows 11 Home 64-bit<\/td>\n<\/tr>\n<tr>\n<th>Processor:<\/th>\n<td>Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz, 3192 Mhz, 6 Core(s), 12 Logical Processor(s)<\/td>\n<\/tr>\n<tr>\n<th>Physical RAM:<\/th>\n<td>31.9 GB<\/td>\n<\/tr>\n<tr>\n<th>Device (GPU):<\/th>\n<td>NVIDIA GeForce GTX 1050 Ti<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Once the computer has been selected, we install TensorFlow (2.1.0) and Neural Designer (5.9.9) on it.<\/p>\n<p>Below, the TensorFlow code used is shown.<\/p>\n<\/p>\n<pre style=\"margin: 0; line-height: 125%;\">import tensorflow as tf\nimport pandas as pd\nimport time\nimport numpy as np\nfrom tensorflow.keras.utils import Sequence\n#read data float32\nfilename = \"C:\/Users\/Usuario\/Downloads\/rosenbrock.csv\"\ndf_test = pd.read_csv(filename, nrows=100)\nfloat_cols = [c for c in df_test if df_test[c].dtype == \"float64\"]\nfloat32_cols = {c: np.float32 for c in float_cols}\ndata = pd.read_csv(filename, engine='c', dtype=float32_cols)\nx = data.iloc[:,:-1].values\ny = data.iloc[:,[-1]].values\ninitializer = tf.keras.initializers.RandomUniform(minval=-1., maxval=1.)\n#build model\nmodel = tf.keras.models.Sequential([tf.keras.layers.Dense(1000,activation = 'tanh', kernel_initializer = initializer, bias_initializer=initializer),\ntf.keras.layers.Dense(1, activation = 'linear', kernel_initializer = initializer, bias_initializer=initializer)])\t   \t\n#compile model\nmodel.compile(optimizer='adam', loss = 'mean_squared_error')\n#train model \nclass DataGenerator(Sequence):\n                def __init__(self, x_set, y_set, batch_size):\n                self.x, self.y = x_set, y_set\n                self.batch_size = batch_size\n                def __len__(self):\n                return int(np.ceil(len(self.x) \/ float(self.batch_size)))\n                def __getitem__(self, idx):\n        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n        batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n                return batch_x, batch_y\ntrain_gen = DataGenerator(x, y, 1000)\nstart_time = time.time()\nwith tf.device('\/gpu:0'):\n    history = model.fit(train_gen, epochs=20000)\nprint(\"Training time: \", round(time.time() - start_time), \" seconds\")\n<\/pre>\n<\/div><div class=\"ndb-card\" id=\"Referenceelectricityconsumptionmeter\"><h2>Reference electricity consumption meter<\/h2><p>This section describes the device used for the energy consumption measurements so that the reader can reproduce the results obtained in the following section with maximum accuracy.<\/p>\n<table>\n<tbody>\n<tr>\n<th>Device model<\/th>\n<td>Perel E305EM5-G<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><div class=\"ndb-card\" id=\"Results\"><h2>Results<\/h2><p>The last step is to run the benchmark application on the selected machine with TensorFlow and Neural Designer and compare the energy consumed by those platforms during training.<\/p>\n<p>The following figure shows the training time with TensorFlow.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/07\/capturafinalrecortadarosenbrockgputensorflow-1.webp\" alt=\"\" width=\"820\" height=\"423\" \/><\/p>\n<p>As we can see, TensorFlow takes 30:14:30 to train the neural network for 20000 epochs (5.44 seconds\/epoch).<br \/>The final mean squared error is 0.0003. The overall energy consumption of the training process is 4.5 kWh, as shown below.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/07\/fotoconsumidoresgpurosenbrocktensorflow-2-1024x680.webp\" alt=\"\" width=\"800\" height=\"531\" \/><\/p>\n<p>Finally, the following figure shows the training time with Neural Designer.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/07\/Captura-de-pantalla-rosenbrock-Neural-Designer-recortada-3-653x1024.webp\" alt=\"\" width=\"397\" height=\"623\" \/><\/p>\n<p>Neural Designer takes 21:03:43 to train the neural network for 20000 epochs (3.79 seconds\/epoch). During that time, it reaches a mean squared error of 0.023. The overall energy consumption of the training process is 2.6 kWh, as shown below.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/07\/fotoconsumidoresgpurosenbrockneuraldesigner-4-1024x859.webp\" alt=\"\" width=\"425\" height=\"356\" \/><\/p>\n<p>The following table summarizes the the most important metrics the two machine learning platforms yielded.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00a0<\/th>\n<th>TensorFlow<\/th>\n<th>Neural Designer<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Training time<\/th>\n<td style=\"text-align: right;\">30:14:30<\/td>\n<td style=\"text-align: right;\">21:03:43<\/td>\n<\/tr>\n<tr>\n<th>Epoch time<\/th>\n<td style=\"text-align: right;\">5.44 seconds\/epoch<\/td>\n<td style=\"text-align: right;\">3.79 seconds\/epoch<\/td>\n<\/tr>\n<tr>\n<th>Training speed<\/th>\n<td style=\"text-align: right;\">183,824 samples\/second<\/td>\n<td style=\"text-align: right;\">263.852 samples\/second<\/td>\n<\/tr>\n<tr>\n<th>Total energy consumed<\/th>\n<td style=\"text-align: right;\">4.5kWh<\/td>\n<td style=\"text-align: right;\">2.6 kWh<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The following chart depicts the energy consumed using TensorFlow and Neural Designer graphically in this case.<\/p>\n<p><b>Electric energy consumption<\/b><\/p>\n<p>TensorFlow\u00a0<\/p>\n<p>Neural Designer<\/p>\n<p>\u00a0<\/p>\n<p><\/p>\n<p>As we can see, the energy consumption of Neural Designer for this application is <b>42<\/b> % lower than that of TensorFlow.<\/p>\n<\/div><div class=\"ndb-card ndb-card--accent\" id=\"Conclusions\"><h2>Conclusions<\/h2><p>Neural Designer is entirely written in C ++, uses analytical differentiation, and has been optimized to minimize the number of operations during training.<\/p>\n<p>As a result, its energy consumption during the training process using Neural Designer is <b>42<\/b> % lower than that using TensorFlow.<\/p>\n<p>To reproduce these results, <a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">download\u00a0<\/a>Neural Designer and follow the steps described in this article.<\/p>\n<\/div><\/div><\/div>","protected":false},"author":16,"featured_media":2233,"template":"","categories":[],"tags":[37],"class_list":["post-3384","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-platforms"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Energy consumption of TensorFlow and Neural Designer<\/title>\n<meta name=\"description\" content=\"Compare the energy consumption of TensorFlow and Neural Designer using the GPU for an approximation benchmark.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/blog\/energy-consumption-comparison\/\" \/>\n<meta property=\"og:locale\" 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